Add files using upload-large-folder tool

output/preprocess/HIV_Resistance/clinical_data/GSE33580.csv

@@ -1,2 +1,2 @@
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output/preprocess/HIV_Resistance/code/GSE117748.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+cohort = "GSE117748"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/HIV_Resistance"
+in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE117748"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/HIV_Resistance/GSE117748.csv"
+out_gene_data_file = "./output/z3/preprocess/HIV_Resistance/gene_data/GSE117748.csv"
+out_clinical_data_file = "./output/z3/preprocess/HIV_Resistance/clinical_data/GSE117748.csv"
+json_path = "./output/z3/preprocess/HIV_Resistance/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability (likely not suitable: cell-line based SuperSeries focused on microRNA)
+is_gene_available = False
+
+# 2) Variable availability from the provided Sample Characteristics Dictionary
+trait_row = None    # No HIV resistance info
+age_row = None      # No age info for human subjects
+gender_row = None   # No gender info for human subjects
+
+# 2.2) Converters
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    # Binary: 1=resistant/protected, 0=non-resistant/susceptible. Unknown -> None
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    s = v.strip().lower()
+
+    # Positive/resistant indicators
+    pos_terms = [
+        'hesn', 'hiv-exposed seronegative', 'exposed uninfected', 'exposed-uninfected',
+        'resistant', 'non-susceptible', 'nonsusceptible', 'protected',
+        'long-term nonprogressor', 'long term nonprogressor', 'ltnp',
+        'elite controller', 'viremic controller'
+    ]
+    if any(term in s for term in pos_terms):
+        return 1
+
+    # Negative/susceptible indicators
+    neg_terms = [
+        'susceptible', 'case', 'infected', 'seropositive', 'hiv positive', 'hiv-1 positive',
+        'aids', 'progressor', 'rapid progressor'
+    ]
+    if any(term in s for term in neg_terms):
+        return 0
+
+    # Ambiguous terms: don't force mapping
+    amb_terms = ['control', 'healthy', 'hiv negative', 'hiv-1 negative', 'uninfected', 'naive']
+    if any(term in s for term in amb_terms):
+        return None
+
+    return None
+
+def convert_age(x):
+    # Continuous numeric age in years, unknown -> None
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    s = v.lower()
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: female->0, male->1, unknown -> None
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    s = v.strip().lower()
+    if s in ['male', 'm', 'man', 'boy']:
+        return 1
+    if s in ['female', 'f', 'woman', 'girl']:
+        return 0
+    return None
+
+# 3) Initial filtering metadata save
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row were available:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/HIV_Resistance/code/GSE33580.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+cohort = "GSE33580"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/HIV_Resistance"
+in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE33580"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/HIV_Resistance/GSE33580.csv"
+out_gene_data_file = "./output/z3/preprocess/HIV_Resistance/gene_data/GSE33580.csv"
+out_clinical_data_file = "./output/z3/preprocess/HIV_Resistance/clinical_data/GSE33580.csv"
+json_path = "./output/z3/preprocess/HIV_Resistance/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix HGU133 Plus 2.0 microarray -> mRNA expression data
+
+# 2) Variable availability
+trait_row = 1  # 'hiv status: HIV resistant' vs 'hiv status: HIV negative'
+age_row = None  # Not provided
+gender_row = None  # All participants are women per background; constant feature -> treat as unavailable
+
+# 2.2) Converters
+def _after_colon(val):
+    if pd.isna(val):
+        return None
+    s = str(val)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(val):
+    v = _after_colon(val)
+    if v is None:
+        return None
+    x = v.lower().strip()
+    x = x.replace('-', ' ').replace('_', ' ')
+    # Positive (resistant) mappings
+    resistant_terms = {
+        'hiv resistant', 'resistant', 'resistance',
+        'hiv exposed uninfected', 'exposed uninfected', 'hiv exposed and uninfected',
+        'eu', 'heu'
+    }
+    # Negative/control/susceptible mappings
+    control_terms = {
+        'hiv negative', 'negative', 'control', 'controls',
+        'susceptible', 'hiv susceptible', 'susceptible control', 'negative control'
+    }
+    # Normalize some common phrases
+    if 'resistant' in x or 'exposed' in x and 'uninfected' in x:
+        return 1
+    if 'hiv negative' in x or 'negative' == x or 'control' in x or 'susceptible' in x:
+        return 0
+    if x in resistant_terms:
+        return 1
+    if x in control_terms:
+        return 0
+    return None
+
+def convert_age(val):
+    v = _after_colon(val)
+    if v is None:
+        return None
+    # extract first number
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        if 0 < age < 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(val):
+    v = _after_colon(val)
+    if v is None:
+        return None
+    x = v.lower().strip()
+    # Map female -> 0, male -> 1
+    if any(k in x for k in ['female', 'woman', 'women', 'girl', 'f']):
+        return 0
+    if any(k in x for k in ['male', 'man', 'men', 'boy', 'm']):
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (since trait_row is available)
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=None,
+    gender_row=gender_row,
+    convert_gender=None
+)
+
+# Preview and save
+_ = preview_df(selected_clinical_df, n=5)
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns in gene_annotation for probe IDs and gene symbols
+probe_id_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features if any
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+note = "INFO: Age unavailable; Gender constant (all women) per series summary; only trait used as clinical covariate."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/HIV_Resistance/code/GSE46599.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+cohort = "GSE46599"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/HIV_Resistance"
+in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE46599"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/HIV_Resistance/GSE46599.csv"
+out_gene_data_file = "./output/z3/preprocess/HIV_Resistance/gene_data/GSE46599.csv"
+out_clinical_data_file = "./output/z3/preprocess/HIV_Resistance/clinical_data/GSE46599.csv"
+json_path = "./output/z3/preprocess/HIV_Resistance/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Gene expression data availability
+is_gene_available = True  # Whole-genome analysis of ISGs implies gene expression data (not miRNA-only or methylation-only)
+
+# 2) Variable availability and converters
+trait_row = 4  # 'resistance to hiv-1 following ifn treatment'
+age_row = None
+gender_row = None
+
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    try:
+        val = str(x)
+    except Exception:
+        return None
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    val = val.strip()
+    if val == '' or val.lower() in {'na', 'nan', 'none', 'unknown', 'n/a'}:
+        return None
+    return val
+
+def convert_trait(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if 'untreated' in v_low:
+        return None
+    if 'resistant' in v_low and 'partially' not in v_low:
+        return 1
+    if 'permissive' in v_low or 'partially' in v_low:
+        return 0
+    return None
+
+# Age and Gender not available in this dataset
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+observed_ids = [
+    'ILMN_1343291', 'ILMN_1343295', 'ILMN_1651209', 'ILMN_1651228',
+    'ILMN_1651229', 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651236',
+    'ILMN_1651238', 'ILMN_1651253', 'ILMN_1651254', 'ILMN_1651259',
+    'ILMN_1651260', 'ILMN_1651262', 'ILMN_1651268', 'ILMN_1651278',
+    'ILMN_1651281', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651286'
+]
+requires_gene_mapping = any(id_.startswith('ILMN_') for id_ in observed_ids)
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide annotation columns for probe IDs and gene symbols based on preview
+probe_col = 'ID'       # Matches probe identifiers like 'ILMN_1343291'
+gene_symbol_col = 'Symbol'  # Contains gene symbols
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping: convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Ensure clinical features dataframe is available; if not in memory, load from saved CSV
+if 'selected_clinical_df' not in globals() or selected_clinical_df is None:
+    if not os.path.exists(out_clinical_data_file):
+        raise FileNotFoundError(f"Clinical data file not found at: {out_clinical_data_file}")
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2) Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Determine availability flags before filtering/imputation and cast to built-in bool
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and (linked_data[trait].notna().any()))
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Bias checks and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+is_trait_biased = bool(is_trait_biased)  # ensure JSON serializable
+
+# 5) Final validation and save cohort metadata
+note = "INFO: HIV resistance phenotype inferred from 'resistance to hiv-1 following ifn treatment'; untreated samples set to missing."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=bool(is_gene_available),
+    is_trait_available=bool(is_trait_available),
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/HIV_Resistance/code/TCGA.py

@@ -0,0 +1,56 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/HIV_Resistance/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/HIV_Resistance/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/HIV_Resistance/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/HIV_Resistance/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# 1. Select the most appropriate TCGA cohort directory for the trait "HIV_Resistance"
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+keywords = [
+    'hiv', 'aids', 'antiretroviral', 'art', 'haart', 'retroviral',
+    'viral load', 'viral_load', 'seroconversion', 'cd4', 'cd8',
+    'immune deficiency', 'immunodeficiency'
+]
+
+matched_dirs = [d for d in subdirs if any(kw in d.lower() for kw in keywords)]
+
+selected_dir = None
+if matched_dirs:
+    # Prefer the most specific match (choose the one with 'hiv' in name first, else the longest name)
+    hiv_matches = [d for d in matched_dirs if 'hiv' in d.lower()]
+    selected_dir = hiv_matches[0] if hiv_matches else sorted(matched_dirs, key=len, reverse=True)[0]
+
+if selected_dir is None:
+    # No suitable cohort for HIV_Resistance in TCGA; record and skip further processing
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # 2. Identify clinical and genetic file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load both files as DataFrames
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
+
+    # 4. Print clinical column names
+    print(list(clinical_df.columns))

output/preprocess/HIV_Resistance/cohort_info.json

@@ -1,42 +1 @@
-{
-  "GSE46599": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 24
-  },
-  "GSE33580": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 86
-  },
-  "GSE117748": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 15
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 48
-  }
-}
+{"GSE46599": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 24, "note": "INFO: HIV resistance phenotype inferred from 'resistance to hiv-1 following ifn treatment'; untreated samples set to missing."}, "GSE33580": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 86, "note": "INFO: Age unavailable; Gender constant (all women) per series summary; only trait used as clinical covariate."}, "GSE117748": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Hemochromatosis/code/GSE159676.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hemochromatosis"
+cohort = "GSE159676"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hemochromatosis"
+in_cohort_dir = "../DATA/GEO/Hemochromatosis/GSE159676"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hemochromatosis/GSE159676.csv"
+out_gene_data_file = "./output/z3/preprocess/Hemochromatosis/gene_data/GSE159676.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hemochromatosis/clinical_data/GSE159676.csv"
+json_path = "./output/z3/preprocess/Hemochromatosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Determine data availability
+is_gene_available = True  # Affymetrix Human Gene 1.0 ST array -> mRNA expression data
+
+# 2) Variable availability
+trait_row = 0  # 'condition' field includes 'Haemochromatosis' among other conditions
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _extract_value(cell):
+    if cell is None:
+        return None
+    s = str(cell)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    val = val.strip()
+    return val if val != "" else None
+
+def convert_trait(cell):
+    val = _extract_value(cell)
+    if val is None:
+        return None
+    v = val.lower()
+    # Map presence of Hemochromatosis to 1; all other known conditions and healthy to 0
+    if "haemochromatosis" in v or "hemochromatosis" in v:
+        return 1
+    # For other conditions and healthy controls, this is a negative for the trait
+    return 0
+
+def convert_age(cell):
+    val = _extract_value(cell)
+    if val is None:
+        return None
+    # Extract numeric age in years if present
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', val)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(cell):
+    val = _extract_value(cell)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+
+    import os
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1) Decide the columns for mapping
+probe_col = 'ID'  # Matches the probe identifiers in gene_data index (e.g., '7896754')
+gene_symbol_col = 'gene_assignment'  # Contains gene symbols within descriptive strings
+
+# 2) Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort metadata
+note = "INFO: Only trait available (no age/gender). Labels appear highly imbalanced for Hemochromatosis in this series."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hemochromatosis/code/GSE50579.py

@@ -0,0 +1,216 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hemochromatosis"
+cohort = "GSE50579"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hemochromatosis"
+in_cohort_dir = "../DATA/GEO/Hemochromatosis/GSE50579"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hemochromatosis/GSE50579.csv"
+out_gene_data_file = "./output/z3/preprocess/Hemochromatosis/gene_data/GSE50579.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hemochromatosis/clinical_data/GSE50579.csv"
+json_path = "./output/z3/preprocess/Hemochromatosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Determine data availability
+is_gene_available = True  # "Expression profiling" with genome-wide gene expression
+# Variables and their rows in the sample characteristics dictionary
+trait_row = 1     # 'etiology' includes 'genetic hemochromatosis'
+age_row = 5       # 'age (yrs)'
+gender_row = 3    # 'gender'
+is_trait_available = trait_row is not None
+
+# 2) Conversion functions
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(':', 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    v = v.strip()
+    return v if v != '' else None
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    low = v.lower()
+    # Unknowns
+    if low in {'n.d.', 'nd', 'na', 'n/a', 'not determined', 'unknown', ''}:
+        return None
+    # Positive cases
+    if 'hemochromatosis' in low:
+        return 1
+    # Other etiologies -> control/reference for this trait
+    if any(k in low for k in ['cryptogenic', 'alcohol', 'hcv', 'hbv', 'alpha-1 antitrypsin']):
+        return 0
+    # Fallback: if it's some other described etiology, treat as non-hemochromatosis (0)
+    if low not in {'', 'n.d.'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    low = v.lower()
+    if low in {'n.d.', 'nd', 'na', 'n/a', 'not determined', 'unknown', ''}:
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    low = v.lower()
+    if low in {'female', 'f'}:
+        return 0
+    if low in {'male', 'm'}:
+        return 1
+    if low in {'n.d.', 'nd', 'na', 'n/a', 'not determined', 'unknown', ''}:
+        return None
+    return None
+
+# 3) Save initial metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for probe IDs and gene symbols based on availability and content
+id_candidates = ['ID', 'SPOT_ID']
+symbol_candidates = ['GENE_SYMBOL', 'GENE', 'GENE_NAME']
+
+probe_col = next((c for c in id_candidates if c in gene_annotation.columns), None)
+if probe_col is None:
+    raise ValueError("No suitable probe ID column found in gene annotation.")
+
+# Try symbol columns in order until we get a non-empty mapping
+mapping_df = None
+for sym_col in symbol_candidates:
+    if sym_col in gene_annotation.columns:
+        tmp_map = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=sym_col)
+        if not tmp_map.empty:
+            mapping_df = tmp_map
+            break
+
+if mapping_df is None or mapping_df.empty:
+    raise ValueError("No suitable gene symbol column produced a valid mapping from the annotation.")
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Optional debug previews
+print(f"Shapes before missing-value handling -> clinical: {selected_clinical_df.shape}, "
+      f"gene: {normalized_gene_data.shape}, linked: {linked_data.shape}")
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+print(f"Shape after missing-value handling: {linked_data.shape}")
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort information
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Gene symbols normalized and data linked; standard GEO preprocessing pipeline applied."
+)
+
+# 6. Save usable linked data
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hemochromatosis/code/TCGA.py

@@ -0,0 +1,60 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hemochromatosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hemochromatosis/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Hemochromatosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hemochromatosis/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Hemochromatosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Identify the most relevant TCGA cohort directory for Hemochromatosis
+dir_candidates = []
+if os.path.exists(tcga_root_dir):
+    for d in os.listdir(tcga_root_dir):
+        full_path = os.path.join(tcga_root_dir, d)
+        if os.path.isdir(full_path):
+            dir_candidates.append(d)
+
+trait_keywords = ['hemochromatosis', 'haemochromatosis', 'iron overload', 'iron']
+selected_dir = None
+for d in dir_candidates:
+    name_l = d.lower()
+    if any(k in name_l for k in trait_keywords):
+        selected_dir = d
+        break
+
+# If no suitable directory is found, skip this trait and mark as completed
+if selected_dir is None:
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort found for the trait 'Hemochromatosis'. Skipping.")
+else:
+    # Step 2: Identify file paths for clinical and genetic data
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    clinical_compression = 'gzip' if clinical_file_path.endswith('.gz') else None
+    genetic_compression = 'gzip' if genetic_file_path.endswith('.gz') else None
+
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, compression=clinical_compression, low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, compression=genetic_compression, low_memory=False)
+
+    # Step 4: Print column names of the clinical data
+    print(list(clinical_df.columns))

output/preprocess/Hepatitis/clinical_data/GSE114783.csv

@@ -1,2 +1,2 @@
 ,GSM3150135,GSM3150136,GSM3150137,GSM3150138,GSM3150139,GSM3150140,GSM3150141,GSM3150142,GSM3150143,GSM3150144,GSM3150145,GSM3150146,GSM3150147,GSM3150148,GSM3150149,GSM3150150,GSM3150151,GSM3150152,GSM3150153,GSM3150154,GSM3150155,GSM3150156,GSM3150157,GSM3150158,GSM3150159,GSM3150160,GSM3150161,GSM3150162,GSM3150163,GSM3150164,GSM3150165,GSM3150166,GSM3150167,GSM3150168,GSM3150169,GSM3150170
-Hepatitis,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Hepatitis,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Hepatitis/clinical_data/GSE124719.csv

@@ -1,4 +1,3 @@
 ,GSM3543875,GSM3543876,GSM3543877,GSM3543878,GSM3543879,GSM3543880,GSM3543881,GSM3543882,GSM3543883,GSM3543884,GSM3543885,GSM3543886,GSM3543887,GSM3543888,GSM3543889,GSM3543890,GSM3543891,GSM3543892,GSM3543893,GSM3543894,GSM3543895,GSM3543896,GSM3543897,GSM3543898,GSM3543899,GSM3543900,GSM3543901,GSM3543902,GSM3543903,GSM3543904,GSM3543905,GSM3543906,GSM3543907,GSM3543908,GSM3543909,GSM3543910,GSM3543911,GSM3543912,GSM3543913,GSM3543914,GSM3543915,GSM3543916,GSM3543917,GSM3543918,GSM3543919,GSM3543920,GSM3543921,GSM3543922,GSM3543923,GSM3543924,GSM3543925,GSM3543926,GSM3543927,GSM3543928,GSM3543929,GSM3543930,GSM3543931,GSM3543932,GSM3543933,GSM3543934,GSM3543935,GSM3543936,GSM3543937,GSM3543938,GSM3543939,GSM3543940,GSM3543941,GSM3543942,GSM3543943,GSM3543944,GSM3543945,GSM3543946,GSM3543947,GSM3543948,GSM3543949,GSM3543950,GSM3543951,GSM3543952,GSM3543953,GSM3543954,GSM3543955,GSM3543956,GSM3543957,GSM3543958,GSM3543959,GSM3543960,GSM3543961,GSM3543962,GSM3543963,GSM3543964,GSM3543965,GSM3543966,GSM3543967,GSM3543968,GSM3543969,GSM3543970,GSM3543971,GSM3543972,GSM3543973,GSM3543974,GSM3543975,GSM3543976,GSM3543977,GSM3543978,GSM3543979,GSM3543980,GSM3543981,GSM3543982,GSM3543983,GSM3543984,GSM3543985,GSM3543986,GSM3543987,GSM3543988,GSM3543989,GSM3543990,GSM3543991,GSM3543992,GSM3543993,GSM3543994,GSM3543995,GSM3543996,GSM3543997,GSM3543998,GSM3543999,GSM3544000,GSM3544001,GSM3544002,GSM3544003,GSM3544004,GSM3544005,GSM3544006,GSM3544007,GSM3544008,GSM3544009,GSM3544010,GSM3544011,GSM3544012,GSM3544013,GSM3544014,GSM3544015,GSM3544016,GSM3544017,GSM3544018,GSM3544019,GSM3544020,GSM3544021,GSM3544022,GSM3544023,GSM3544024,GSM3544025,GSM3544026,GSM3544027,GSM3544028,GSM3544029,GSM3544030,GSM3544031,GSM3544032,GSM3544033,GSM3544034,GSM3544035,GSM3544036,GSM3544037,GSM3544038,GSM3544039,GSM3544040,GSM3544041,GSM3544042,GSM3544043,GSM3544044,GSM3544045,GSM3544046,GSM3544047,GSM3544048,GSM3544049,GSM3544050,GSM3544051,GSM3544052,GSM3544053
-Hepatitis,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-Age,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28.0,28.0,21.0,21.0,25.0,25.0,29.0,29.0,19.0,19.0,23.0,23.0,24.0,24.0,21.0,21.0,22.0,24.0,24.0,31.0,31.0,33.0,33.0,22.0,22.0,19.0,19.0,27.0,27.0,32.0,32.0,29.0,29.0,29.0,29.0,23.0,23.0,25.0,25.0,22.0,22.0,21.0,21.0,21.0,21.0,25.0,25.0,20.0,20.0,27.0,22.0,22.0,20.0,33.0,33.0,18.0,18.0,36.0,36.0,21.0,21.0,28.0,28.0,21.0,21.0,23.0,23.0,25.0,25.0,22.0,22.0,18.0,18.0,23.0,23.0,23.0,23.0,21.0,21.0,22.0,22.0,21.0,21.0,32.0,32.0,23.0,23.0,22.0,27.0,20.0,,
-Gender,,,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,0.0,0.0,,,,,0.0,0.0,,,0.0,0.0,0.0,0.0,0.0,0.0,,,,0.0,0.0,,,0.0,0.0,0.0,0.0,,,0.0,0.0,,,,,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,
+Hepatitis,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0
+Age,29.0,29.0,21.0,28.0,25.0,25.0,23.0,23.0,19.0,19.0,24.0,24.0,33.0,33.0,24.0,24.0,36.0,36.0,21.0,23.0,25.0,25.0,29.0,29.0,32.0,32.0,22.0,22.0,23.0,21.0,22.0,22.0,19.0,19.0,22.0,22.0,33.0,33.0,25.0,25.0,22.0,22.0,21.0,21.0,22.0,22.0,31.0,31.0,32.0,32.0,22.0,22.0,28.0,28.0,21.0,21.0,29.0,29.0,21.0,21.0,20.0,20.0,21.0,21.0,27.0,27.0,20.0,27.0,27.0,23.0,23.0,23.0,23.0,23.0,23.0,23.0,23.0,18.0,18.0,25.0,25.0,21.0,21.0,18.0,18.0,21.0,21.0,28.0,28.0,21.0,21.0,25.0,25.0,29.0,29.0,19.0,19.0,23.0,23.0,24.0,24.0,21.0,21.0,22.0,24.0,24.0,31.0,31.0,33.0,33.0,22.0,22.0,19.0,19.0,27.0,27.0,32.0,32.0,29.0,29.0,29.0,29.0,23.0,23.0,25.0,25.0,22.0,22.0,21.0,21.0,21.0,21.0,25.0,25.0,20.0,20.0,27.0,22.0,22.0,20.0,33.0,33.0,18.0,18.0,36.0,36.0,21.0,21.0,28.0,28.0,21.0,21.0,23.0,23.0,25.0,25.0,22.0,22.0,18.0,18.0,23.0,23.0,23.0,23.0,21.0,21.0,22.0,22.0,21.0,21.0,32.0,32.0,23.0,23.0,22.0,27.0,20.0,28.0,20.0

output/preprocess/Hepatitis/clinical_data/GSE125860.csv

@@ -1,4 +1,2 @@
-,7,17,18
-Hepatitis,26.712,72.0,
-Age,12.666,33.0,
-Gender,,,0.0
+,GSM3583371,GSM3583372,GSM3583373,GSM3583374,GSM3583375,GSM3583376,GSM3583377,GSM3583378,GSM3583379,GSM3583380,GSM3583381,GSM3583382,GSM3583383,GSM3583384,GSM3583385,GSM3583386,GSM3583387,GSM3583388,GSM3583389,GSM3583390,GSM3583391,GSM3583392,GSM3583393,GSM3583394,GSM3583395,GSM3583396,GSM3583397,GSM3583398,GSM3583399,GSM3583400,GSM3583401,GSM3583402,GSM3583403,GSM3583404,GSM3583405,GSM3583406,GSM3583407,GSM3583408,GSM3583409,GSM3583410,GSM3583411,GSM3583412,GSM3583413,GSM3583414,GSM3583415,GSM3583416,GSM3583417,GSM3583418,GSM3583419,GSM3583420,GSM3583421,GSM3583422,GSM3583423,GSM3583424,GSM3583425,GSM3583426,GSM3583427,GSM3583428,GSM3583429,GSM3583430,GSM3583431,GSM3583432,GSM3583433,GSM3583434,GSM3583435,GSM3583436,GSM3583437,GSM3583438,GSM3583439,GSM3583440,GSM3583441,GSM3583442,GSM3583443,GSM3583444,GSM3583445,GSM3583446,GSM3583447,GSM3583448,GSM3583449,GSM3583450,GSM3583451,GSM3583452,GSM3583453,GSM3583454,GSM3583455,GSM3583456,GSM3583457,GSM3583458,GSM3583459,GSM3583460,GSM3583461,GSM3583462,GSM3583463,GSM3583464,GSM3583465,GSM3583466,GSM3583467,GSM3583468,GSM3583469,GSM3583470,GSM3583471,GSM3583472,GSM3583473,GSM3583474,GSM3583475,GSM3583476,GSM3583477,GSM3583478,GSM3583479,GSM3583480,GSM3583481,GSM3583482,GSM3583483,GSM3583484,GSM3583485,GSM3583486,GSM3583487,GSM3583488,GSM3583489,GSM3583490,GSM3583491,GSM3583492,GSM3583493,GSM3583494,GSM3583495,GSM3583496,GSM3583497,GSM3583498,GSM3583499,GSM3583500,GSM3583501,GSM3583502,GSM3583503,GSM3583504,GSM3583505,GSM3583506,GSM3583507,GSM3583508,GSM3583509,GSM3583510,GSM3583511,GSM3583512,GSM3583513,GSM3583514,GSM3583515,GSM3583516,GSM3583517,GSM3583518,GSM3583519,GSM3583520,GSM3583521,GSM3583522,GSM3583523,GSM3583524,GSM3583525,GSM3583526,GSM3583527,GSM3583528,GSM3583529,GSM3583530,GSM3583531,GSM3583532,GSM3583533,GSM3583534,GSM3583535,GSM3583536,GSM3583537,GSM3583538,GSM3583539,GSM3583540,GSM3583541,GSM3583542,GSM3583543
+Hepatitis,34.882,5.0,5.072,5.0,6.738,5.0,5.0,5.0,106.136,5.0,5.0,6.757,5.0,5.0,148.805,5.0,5.0,5.0,5.0,5.0,26.712,54.976,5.0,,142.442,5.0,67.995,9.376,5.0,5.0,5.0,19.557,78.414,5.0,5.0,5.0,16938.23,5.0,5.0,5.466,5.0,5.0,12.666,5.512,5.0,5.0,5.0,366.395,11.966,5.0,6.74,10.763,53.131,27.114,12.091,36.768,7.124,5.0,5.0,5.0,5.0,63.196,5416.394,7589.005,5.0,5.0,1120.902,5.0,5.0,46.87,30320.357,34.839,5.0,15.851,5.0,262.988,25.298,5.0,5.0,124.499,39.224,16.269,5.0,212.134,102.117,212.208,5.0,7.774,5.0,5.0,5.0,5.0,200.656,5.0,5.0,5.0,5.0,5.0,13.633,8.275,18.207,5.0,5.0,5.0,5.0,19.705,5.0,5.0,5.0,5.0,33.203,5.0,5.0,5.0,5.0,5.0,5.665,5.0,5.0,5.0,5.0,20.055,26.092,59.28,9661.057,5.0,36979.5,13.896,5.0,52.148,17311.4,5.0,62.815,5.0,172.776,5.0,48.631,5.0,5.0,5.0,5.0,5.0,317.022,5.887,291.133,5.0,2029.989,14.454,11.518,118.492,9.368,5.0,5.0,5.0,5.0,2936.73,15.003,5.0,5.0,34.028,5.0,8.414,5.0,11.406,5.0,8.471,90.739,5.0,5.0,11.444,5.0,1482.147,5.0

output/preprocess/Hepatitis/clinical_data/GSE159676.csv

@@ -1,2 +1,2 @@
 ,GSM4837490,GSM4837491,GSM4837492,GSM4837493,GSM4837494,GSM4837495,GSM4837496,GSM4837497,GSM4837498,GSM4837499,GSM4837500,GSM4837501,GSM4837502,GSM4837503,GSM4837504,GSM4837505,GSM4837506,GSM4837507,GSM4837508,GSM4837509,GSM4837510,GSM4837511,GSM4837512,GSM4837513,GSM4837514,GSM4837515,GSM4837516,GSM4837517,GSM4837518,GSM4837519,GSM4837520,GSM4837521,GSM4837522
-Hepatitis,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Hepatitis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0

output/preprocess/Hepatitis/clinical_data/GSE45032.csv

@@ -1,4 +1,4 @@
-Sample_1,Sample_2,Sample_3,Sample_4,Sample_5,Sample_6,Sample_7,Sample_8,Sample_9,Sample_10,Sample_11,Sample_12,Sample_13,Sample_14,Sample_15,Sample_16,Sample_17,Sample_18,Sample_19,Sample_20,Sample_21,Sample_22,Sample_23,Sample_24,Sample_25,Sample_26,Sample_27
-1.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,
-67.0,56.0,76.0,79.0,66.0,70.0,68.0,72.0,62.0,55.0,71.0,73.0,74.0,61.0,54.0,64.0,59.0,69.0,25.0,41.0,50.0,58.0,49.0,63.0,60.0,52.0,51.0
-1.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM1096016,GSM1096017,GSM1096018,GSM1096019,GSM1096020,GSM1096021,GSM1096022,GSM1096023,GSM1096024,GSM1096025,GSM1096026,GSM1096027,GSM1096028,GSM1096029,GSM1096030,GSM1096031,GSM1096032,GSM1096033,GSM1096034,GSM1096035,GSM1096036,GSM1096037,GSM1096038,GSM1096039,GSM1096040,GSM1096041,GSM1096042,GSM1096043,GSM1096044,GSM1096045,GSM1096046,GSM1096047,GSM1096048,GSM1096049,GSM1096050,GSM1096051,GSM1096052,GSM1096053,GSM1096054,GSM1096055,GSM1096056,GSM1096057,GSM1096058,GSM1096059,GSM1096060,GSM1096061,GSM1096062,GSM1096063
+Hepatitis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,67.0,56.0,76.0,79.0,66.0,70.0,68.0,72.0,62.0,66.0,55.0,62.0,71.0,73.0,74.0,61.0,54.0,64.0,68.0,59.0,79.0,69.0,59.0,71.0,64.0,55.0,66.0,56.0,66.0,68.0,25.0,41.0,50.0,56.0,66.0,58.0,67.0,49.0,63.0,70.0,60.0,50.0,58.0,61.0,60.0,59.0,52.0,51.0
+Gender,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0

output/preprocess/Hepatitis/clinical_data/GSE66843.csv

@@ -1,2 +1,2 @@
-,GSM1633236,GSM1633237,GSM1633238,GSM1633239,GSM1633240,GSM1633241,GSM1633242,GSM1633243,GSM1633244,GSM1633245,GSM1633246,GSM1633247,GSM1633248,GSM1633249,GSM1633250,GSM1633251,GSM1633252
-Hepatitis,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0
+GSM1633236,GSM1633237,GSM1633238,GSM1633239,GSM1633240,GSM1633241,GSM1633242,GSM1633243,GSM1633244,GSM1633245,GSM1633246,GSM1633247,GSM1633248,GSM1633249,GSM1633250,GSM1633251,GSM1633252
+0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0

output/preprocess/Hepatitis/code/GSE114783.py

@@ -0,0 +1,268 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE114783"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE114783"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE114783.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE114783.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE114783.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # "Microarray gene expression" on PBMCs suggests gene expression data is available.
+
+# 2. Variable Availability and Data Type Conversion
+# From the Sample Characteristics Dictionary, diagnosis is at key 0.
+trait_row = 0
+age_row = None  # No age information available
+gender_row = None  # No gender information available
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return str(x)
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    """
+    Binary mapping for 'Hepatitis':
+    - 1: chronic hepatitis B (active hepatitis)
+    - 0: healthy control, hepatitis B virus carrier, liver cirrhosis, hepatocellular carcinoma
+    Unknown/other -> None
+    """
+    val = _extract_value(x)
+    if val is None or val == '':
+        return None
+    v = val.lower()
+    if ('chronic hepatitis' in v) or (v.strip() == 'chb'):
+        return 1
+    if any(k in v for k in ['healthy control', 'hepatitis b virus carrier', 'hbv carrier',
+                            'liver cirrhosis', 'cirrhosis', 'hepatocellular carcinoma', 'hcc']):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None or val == '':
+        return None
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', val)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        return age
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None or val == '':
+        return None
+    v = val.lower()
+    if v in ['male', 'm', 'man']:
+        return 1
+    if v in ['female', 'f', 'woman', 'women']:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if clinical data available)
+if is_trait_available:
+    assert 'clinical_data' in globals(), "clinical_data is required for clinical feature extraction but is not defined."
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Based on the observed identifiers (e.g., AB000409), which are GenBank accessions, mapping to human gene symbols is required.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import re
+
+# Determine which annotation column matches the gene/probe identifiers in gene_data
+annot_cols = list(gene_annotation.columns)
+expr_ids = set(gene_data.index.astype(str))
+
+best_id_col = None
+best_overlap = -1
+for col in annot_cols:
+    col_vals = set(gene_annotation[col].astype(str).str.strip())
+    overlap = len(expr_ids & col_vals)
+    if overlap > best_overlap:
+        best_overlap = overlap
+        best_id_col = col
+
+# Try to find a gene symbol column
+symbol_candidates = [c for c in annot_cols if 'symbol' in str(c).lower()]
+gene_symbol_col = symbol_candidates[0] if len(symbol_candidates) > 0 else None
+
+if gene_symbol_col is not None:
+    # Use existing library functions when a proper symbol column exists
+    mapping_df = get_gene_mapping(gene_annotation, prob_col=best_id_col, gene_col=gene_symbol_col)
+    gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+else:
+    # Fallback: aggregate by Entrez Gene IDs (GENE_ID) when gene symbols are unavailable
+    if 'GENE_ID' not in gene_annotation.columns:
+        raise ValueError("No gene symbol column or GENE_ID column found in the annotation to perform mapping.")
+
+    def split_gene_ids(val: str):
+        s = str(val).strip()
+        if s.lower() in ['', 'nan', 'none']:
+            return []
+        # Single numeric (possibly with .0)
+        if re.fullmatch(r'\d+(\.0)?', s):
+            return [s.split('.', 1)[0]]
+        # Split on common delimiters
+        parts = re.split(r'[;,/| ]+', s)
+        ids = []
+        for p in parts:
+            p = p.strip()
+            if p == '' or p.lower() == 'na':
+                continue
+            if re.fullmatch(r'\d+(\.0)?', p):
+                p = p.split('.', 1)[0]
+            if re.fullmatch(r'\d+', p):
+                ids.append(p)
+        return list(dict.fromkeys(ids))
+
+    # Build mapping df using probe ID and Entrez Gene ID
+    mapping_df = gene_annotation[[best_id_col, 'GENE_ID']].dropna().copy()
+    mapping_df[best_id_col] = mapping_df[best_id_col].astype(str).str.strip()
+
+    # Keep only probes present in expression data
+    mapping_df = mapping_df[mapping_df[best_id_col].isin(gene_data.index)]
+
+    # Split gene IDs and explode
+    mapping_df['Gene'] = mapping_df['GENE_ID'].apply(split_gene_ids)
+    mapping_df['num_genes'] = mapping_df['Gene'].apply(lambda lst: len(lst))
+    mapping_df = mapping_df.explode('Gene')
+    mapping_df = mapping_df.dropna(subset=['Gene'])
+    mapping_df = mapping_df[mapping_df['Gene'] != '']
+    mapping_df = mapping_df.set_index(best_id_col)
+
+    # Join with expression and distribute values across multiple genes per probe
+    merged_df = mapping_df.join(gene_data, how='inner')
+    expr_cols = [c for c in merged_df.columns if c not in ['GENE_ID', 'Gene', 'num_genes']]
+    merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)
+
+    # Sum to gene-level using Entrez IDs as gene labels
+    gene_data = merged_df.groupby('Gene')[expr_cols].sum()
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols; fallback if normalization drops almost all rows (likely Entrez IDs rather than symbols)
+try:
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data.copy())
+    retention_ratio = (len(normalized_gene_data) / len(gene_data)) if len(gene_data) > 0 else 0.0
+    if normalized_gene_data.empty or retention_ratio < 0.05:
+        normalized_gene_data = gene_data.copy()
+        norm_note = f"INFO: Gene symbol normalization yielded low retention (ratio={retention_ratio:.4f}); retained original identifiers (likely Entrez IDs)."
+    else:
+        norm_note = f"INFO: Gene symbols normalized using synonym map (retention_ratio={retention_ratio:.4f})."
+except Exception as e:
+    # If normalization fails for any reason, keep original gene_data
+    normalized_gene_data = gene_data.copy()
+    norm_note = f"WARNING: Gene symbol normalization failed ({e}); retained original identifiers (likely Entrez IDs)."
+
+# Ensure output directory exists and save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check bias and remove biased demographic features if needed
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=norm_note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/code/GSE124719.py

@@ -0,0 +1,221 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE124719"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE124719"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE124719.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE124719.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE124719.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+from typing import Optional
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Gene expression arrays in blood and muscle are described in the series.
+
+# 2) Variable availability and conversion functions
+
+# Identify rows from the sample characteristics dictionary:
+# - Trait (Hepatitis-related): use treatment assignment -> row 1 ('treatment: FENDRIXE' (HepB), 'FLUADE', 'PLACEBOE')
+trait_row = 1
+
+# - Age: explicit age values -> row 11 (primary), with backup values in row 7
+age_row = 11
+backup_age_row = 7
+
+# - Gender: all subjects are male (rows 6/10 show only male), so it's constant -> not available
+gender_row = None
+
+# Conversion helpers
+def _after_colon(val: str) -> Optional[str]:
+    if not isinstance(val, str):
+        return None
+    parts = val.split(":", 1)
+    s = parts[1] if len(parts) > 1 else parts[0]
+    s = s.strip()
+    return s if s else None
+
+def convert_trait(val):
+    """
+    Binary: 1 = Hepatitis B vaccine (FENDRIX/FENDRIXE/HBV/HepB), 0 = others (FLUADE/influenza, PLACEBOE/saline).
+    Unknown -> None
+    """
+    s = _after_colon(val)
+    if s is None:
+        return None
+    sl = s.lower().strip()
+    # Exact token checks
+    if sl in {"fendrix", "fendrixe"}:
+        return 1
+    if sl in {"flua", "fluae", "fluade", "placebo", "placeboe", "saline"}:
+        return 0
+    # Heuristic fallbacks
+    if ("fendrix" in sl) or ("hepb" in sl) or ("hbv" in sl) or ("hepatitis" in sl):
+        return 1
+    if ("placebo" in sl) or ("saline" in sl) or ("flua" in sl) or ("influenza" in sl) or ("flu" in sl):
+        return 0
+    return None
+
+def convert_age(val):
+    """
+    Continuous (years). Extract leading numeric from strings like 'age: 21y' -> 21.0
+    Unknown -> None
+    """
+    s = _after_colon(val)
+    if s is None:
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(val):
+    """
+    Binary: female -> 0, male -> 1, Unknown -> None
+    Note: Gender is constant (all male) in this cohort, so this function won't be used.
+    """
+    s = _after_colon(val)
+    if s is None:
+        return None
+    sl = s.lower()
+    if "male" in sl or sl == "m":
+        return 1
+    if "female" in sl or sl == "f":
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Fill missing ages using backup row (row 7)
+    try:
+        backup_age_series = clinical_data.loc[backup_age_row].apply(convert_age)
+        backup_age_series = pd.to_numeric(backup_age_series, errors='coerce')
+        if "Age" in clinical_features.index:
+            clinical_features.loc["Age"] = clinical_features.loc["Age"].fillna(backup_age_series)
+    except Exception:
+        # If backup filling fails, proceed with original ages
+        pass
+
+    preview = preview_df(clinical_features, n=5)
+    print(preview)
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    clinical_features.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide on identifier and gene symbol columns based on previews:
+id_col = 'ID'             # Matches the probe/row identifiers in gene_data
+gene_symbol_col = 'GENE_SYMBOL'  # Contains human gene symbols
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save normalized gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and demographic features are severely biased
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort information
+note = "INFO: All participants are male; Gender not available. Trait defined as HepB vaccine (FENDRIX/FENDRIXE) vs others."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save the linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/code/GSE125860.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE125860"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE125860"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE125860.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE125860.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE125860.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix transcriptomic profiling mentioned in background
+
+# 2) Variable availability and converters based on provided sample characteristics
+# From the provided dictionary, we select:
+# - Trait (Hepatitis): use "hepatitis b average concentration (post-vax)" at key 7 as continuous
+trait_row = 7
+
+# Age/Gender not observed in the provided sample characteristics snippet
+age_row = None
+gender_row = None
+
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    return s if s != '' else None
+
+def convert_trait(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {'na', 'n/a', 'none', ''}:
+        return None
+    # Handle inequality values like "<5" or ">10"
+    if v_low.startswith('<') or v_low.startswith('>'):
+        num_str = v_low[1:].strip()
+        try:
+            # Map to the threshold value itself for a conservative approximation
+            return float(num_str)
+        except:
+            pass
+    # Try direct float conversion
+    try:
+        return float(v_low)
+    except:
+        # Fallback: extract any numeric substring
+        m = re.search(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', v_low)
+        if m:
+            try:
+                return float(m.group(0))
+            except:
+                return None
+        return None
+
+def convert_age(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {'na', 'n/a', 'none', ''}:
+        return None
+    # Extract numeric age (years)
+    m = re.search(r'[-+]?\d*\.?\d+', v_low)
+    if m:
+        try:
+            return float(m.group(0))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if v_low in {'male', 'm', 'man', 'men'}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Based on the observed identifiers (e.g., 'AFFX-BioB-3_at'), these are Affymetrix probe set/control IDs, not human gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide identifier and gene symbol columns based on annotation preview
+probe_col = 'ID'          # Matches probe IDs like 'AFFX-BioB-3_at'
+gene_symbol_col = 'GeneSymbol'  # Gene symbols column
+
+# 2. Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional normalization of gene symbols to standardized symbols and aggregation
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols (already done in Step 6); just save to file safely.
+normalized_gene_data = gene_data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Probe-to-gene mapping applied and gene symbols normalized in Step 6; "
+        "age and gender not available in this series; trait is continuous HBV post-vaccination "
+        "antibody concentration (mIU/mL) with left-censored values (e.g., '<5') mapped to the threshold.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/code/GSE152738.py

@@ -0,0 +1,130 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE152738"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE152738"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE152738.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE152738.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE152738.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix Human U133 Plus 2 arrays -> mRNA expression
+
+# 2) Variable availability based on provided sample characteristics
+# Sample Characteristics Dictionary:
+# {0: ['age stage: Old (>40 years)', 'age stage: Young (<40 years)'], 1: ['tissue: liver']}
+trait_row = None          # Hepatitis status not available in this dataset
+age_row = 0               # Age stage available (Old vs Young)
+gender_row = None         # No gender information
+
+# 2.2) Converters
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    return x
+
+def convert_trait(x):
+    # Binary: 1 = hepatitis present, 0 = no hepatitis, None = unknown
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).lower()
+
+    # Strong positives
+    positive_keywords = [
+        'hepatitis', 'hbv', 'hcv', 'hep b', 'hep c', 'b virus', 'c virus', 'viral hepatitis'
+    ]
+    # Strong negatives
+    negative_keywords = [
+        'healthy', 'control', 'non-hepatitis', 'no hepatitis', 'hbv-', 'hcv-', 'negative', 'no hcv', 'no hbv'
+    ]
+
+    # If both appear (unlikely), prioritize explicit negatives first
+    if any(k in s for k in negative_keywords):
+        return 0
+    if any(k in s for k in positive_keywords):
+        return 1
+
+    return None
+
+def convert_age(x):
+    # Binary: 1 = Older (>=40 or contains 'old'), 0 = Younger (<40 or contains 'young'), None = unknown
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).lower()
+
+    if 'old' in s:
+        return 1
+    if 'young' in s:
+        return 0
+
+    # Try to parse a numeric age if present
+    nums = re.findall(r'\d+(?:\.\d+)?', s)
+    if nums:
+        try:
+            age_val = float(nums[0])
+            return 1 if age_val >= 40 else 0
+        except Exception:
+            pass
+
+    return None
+
+def convert_gender(x):
+    # Binary: 1 = male, 0 = female, None = unknown
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).lower()
+    if 'male' in s and 'female' not in s:
+        return 1
+    if 'female' in s and 'male' not in s:
+        return 0
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction is skipped because trait_row is None

output/preprocess/Hepatitis/code/GSE159676.py

@@ -0,0 +1,199 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE159676"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE159676"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE159676.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE159676.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE159676.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step: Dataset Analysis and Clinical Feature Extraction for GSE159676
+
+import os
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix Human Gene 1.0 ST array indicates mRNA expression data
+
+# 2) Variable availability and converters
+# From the provided Sample Characteristics Dictionary, only key 0 ("condition") is available and variable.
+trait_row = 0
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    # Map to binary: hepatitis (1) vs non-hepatitis/others (0)
+    if x is None:
+        return None
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    v = val.strip().lower()
+
+    if v in {'', 'na', 'n/a', 'unknown'}:
+        return None
+
+    # Explicit negatives
+    if 'steatohepatitis' in v:  # e.g., non alcoholic steatohepatitis (NASH)
+        return 0
+    if any(k in v for k in [
+        'primary sclerosing cholangitis', 'psc',
+        'primary biliary cirrhosis', 'primary biliary cholangitis', 'pbc',
+        'haemochromatosis',
+        'alcohol related',
+        'healthy', 'normal', 'control'
+    ]):
+        return 0
+
+    # Explicit hepatitis positives
+    if any(k in v for k in [
+        'autoimmune hepatitis',
+        'hepatitis b', 'hbv',
+        'hepatitis c', 'hcv',
+        'viral hepatitis',
+        'hepatitis '
+    ]) or v == 'hepatitis':
+        return 1
+
+    # Default to non-hepatitis for other liver diseases
+    return 0
+
+convert_age = None
+convert_gender = None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The observed identifiers are numeric probe IDs, not standard human gene symbols.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Map probe IDs to gene symbols using annotation, then aggregate probe-level data to gene-level.
+
+# 1) Decide columns:
+# - Expression uses numeric probe IDs matching the 'ID' column in annotation.
+# - Gene symbols are embedded in the 'gene_assignment' column.
+probe_col = 'ID'
+gene_col = 'gene_assignment'
+
+# 2) Build mapping dataframe (ID -> Gene text)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3) Apply mapping to convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# Step 7: Data Normalization and Linking
+
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and saving cohort info
+try:
+    gene_avail = is_gene_available
+except NameError:
+    gene_avail = True  # Based on platform and previous steps
+
+try:
+    trait_avail = is_trait_available
+except NameError:
+    trait_avail = True  # Trait row was identified earlier
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=gene_avail,
+    is_trait_available=trait_avail,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=""
+)
+
+# 6. Save linked dataset if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/code/GSE168049.py

@@ -0,0 +1,219 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE168049"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE168049"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE168049.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE168049.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE168049.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import math
+
+# 1) Gene expression availability
+is_gene_available = True  # mRNA mentioned in the series title (microRNA present too, but mRNA indicates gene expression)
+
+# 2) Variable availability (from Sample Characteristics Dictionary)
+# trait (Hepatitis) is constant: all samples are HBV-ACLF -> not useful for association within this dataset
+trait_row = None
+age_row = 3
+gender_row = 2
+
+# 2.2) Conversion functions
+def _extract_value(cell):
+    if cell is None:
+        return None
+    if isinstance(cell, float) and math.isnan(cell):
+        return None
+    s = str(cell)
+    parts = s.split(":", 1)
+    val = parts[1].strip() if len(parts) > 1 else s.strip()
+    return val if val != "" else None
+
+def convert_trait(x):
+    # Binary: 1 = hepatitis-related case, 0 = control
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Heuristic mappings
+    if any(k in v for k in ["hbv", "hepatitis", "aclf", "liver failure"]):
+        return 1
+    if any(k in v for k in ["healthy", "control", "normal", "non-hepatitis"]):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years
+    val = _extract_value(x)
+    if val is None:
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    if not m:
+        return None
+    age = float(m.group(1))
+    if age < 0 or age > 120:
+        return None
+    return age
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"male", "m", "man"}:
+        return 1
+    if v in {"female", "f", "woman"}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip if trait_row is None)
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide the identifier and gene symbol columns based on the annotation preview:
+# Probe ID column: 'ID'; Gene symbol column: 'GENE_SYMBOL'
+prob_col = 'ID'
+gene_col = 'GENE_SYMBOL'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# Convert probe-level data to gene-level expression
+probe_data = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine trait availability from previous steps
+is_trait_available = ('trait_row' in globals()) and (trait_row is not None)
+
+# If trait is not available, record metadata and stop before linking
+if not is_trait_available:
+    # Record that gene data exists but trait data is unavailable; skip linking
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+    linked_data = None
+    is_usable = False
+else:
+    # 2. Extract clinical features (recompute here to ensure availability)
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row if 'age_row' in globals() else None,
+        convert_age=convert_age if 'convert_age' in globals() else None,
+        gender_row=gender_row if 'gender_row' in globals() else None,
+        convert_gender=convert_gender if 'convert_gender' in globals() else None
+    )
+
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias assessment and removal of biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    note = "INFO: Clinical features extracted and linked with normalized gene expression."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/code/GSE45032.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE45032"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE45032"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE45032.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE45032.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE45032.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Gene expression availability based on series description
+is_gene_available = True  # Microarray gene expression per summary/title
+
+# 2) Variable availability and data type conversion
+# Keys identified from Sample Characteristics Dictionary:
+# 0: cell type (hepatocellular carcinoma vs chronic hepatitis type C) -> trait
+# 2: gender
+# 3: age(yrs)
+trait_row = 0
+age_row = 3
+gender_row = 2
+
+# 2.2 Define conversion functions
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+
+    # Normalize common variations/misspellings
+    v_low = v_low.replace("hepatocallular", "hepatocellular")  # handle misspelling in dict
+
+    # Map to hepatitis (1) vs non-hepatitis (0)
+    # "chronic hepatitis type c" (CHC) -> 1
+    # "hepatocellular carcinoma" (HCC) -> 0
+    if "hepatitis" in v_low:
+        return 1
+    if "hepatocellular carcinoma" in v_low or v_low == "hcc":
+        return 0
+    if v_low in {"chc", "chronic hepatitis", "chronic hepatitis c", "chronic hepatitis type c"}:
+        return 1
+
+    # Unknown label
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    # Strip potential units and convert to float
+    try:
+        # Keep digits and dot only from the value
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        return float(m.group()) if m else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+    if v_low in {"male", "m", "man"}:
+        return 1
+    if v_low in {"female", "f", "woman"}:
+        return 0
+    return None
+
+# Determine trait availability
+is_trait_available = trait_row is not None
+
+# 3) Initial filtering and save metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction, preview, and save
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Clinical features preview:", clinical_preview)
+
+    import os
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping: probe identifiers are in 'ID', gene symbols are in 'GeneName'
+# 1) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GeneName')
+
+# 2) Apply mapping to convert probe-level data to gene-level expression
+probe_data = gene_data  # preserve original probe-level data
+gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Trait encoding for Hepatitis: CHC=1 (chronic hepatitis C), HCC=0 (hepatocellular carcinoma)."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/code/GSE66843.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE66843"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE66843"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE66843.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE66843.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE66843.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Likely mRNA expression in this series (cell-based model), not miRNA-only or methylation-only.
+
+# 2) Variable availability and conversion functions
+# Use infection status as the trait (key 1). Age and gender are not applicable in a cell line model.
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    return x
+
+def convert_trait(x):
+    # Binary: 1 = HCV infection, 0 = control/mock/uninfected
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if any(t in s for t in ["na", "not available", "unknown", "n/a", "none"]):
+        return None
+    if "mock" in s or "control" in s or "uninfected" in s:
+        return 0
+    if "hcv" in s or "infect" in s:
+        return 1
+    return None
+
+def convert_age(x):
+    # Continuous: extract number in years (not applicable here; defined for interface completeness)
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if any(t in s for t in ["na", "not available", "unknown", "n/a", "none"]):
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", s)
+    if m:
+        try:
+            return float(m.group())
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1 (not applicable here; defined for interface completeness)
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if any(t in s for t in ["na", "not available", "unknown", "n/a", "none"]):
+        return None
+    if s.startswith("f"):
+        return 0
+    if s.startswith("m"):
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for mapping based on previous previews:
+# Probe identifiers: 'ID' (e.g., ILMN_1343***)
+# Gene symbols: 'Symbol'
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and demographic features are severely biased
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Derive availability flags based on processed data
+is_gene_available_final = (normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in unbiased_linked_data.columns) and (len(unbiased_linked_data) > 0)
+
+# 5. Final quality validation and save cohort information
+note = ("INFO: Cell-line HCV infection model (Huh7.5.1). Trait derived from infection status; "
+        "no Age/Gender available. ILMN probes mapped via 'Symbol' and gene symbols normalized.")
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/code/GSE85550.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE85550"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE85550"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE85550.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE85550.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE85550.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Gene expression availability
+is_gene_available = True  # Liver biopsy transcriptomic profiling; not miRNA/methylation-only by description.
+
+# 2) Variable availability and converters
+# Based on the sample characteristics dictionary, only patient IDs, tissue, and time_point are present.
+trait_row = None    # No hepatitis status provided; likely all have liver disease and no variation recorded.
+age_row = None      # No age field available.
+gender_row = None   # No gender field available.
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    # Binary mapping for hepatitis status if ever present:
+    # 1 = hepatitis (HBV/HCV/other viral hepatitis/chronic hepatitis), 0 = non-hepatitis/healthy/control.
+    v = _after_colon(x).lower()
+    if v in ("", "na", "n/a", "none", "unknown"):
+        return None
+    # Positive cases
+    pos_keywords = ["hepatitis", "hbv", "hcv", "hbc", "hepatitis b", "hepatitis c", "chronic hepatitis", "acute hepatitis"]
+    if any(k in v for k in pos_keywords) or v in ("yes", "case", "disease", "patient"):
+        return 1
+    # Negative cases
+    neg_keywords = ["healthy", "control", "non-hepatitis", "no hepatitis", "normal"]
+    if any(k in v for k in neg_keywords) or v in ("no", "ctrl"):
+        return 0
+    # Ambiguous tokens
+    return None
+
+def convert_age(x):
+    v = _after_colon(x).lower()
+    if v in ("", "na", "n/a", "none", "unknown"):
+        return None
+    # Extract first numeric token; handle units like years/yrs/y/o
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x).lower()
+    if v in ("", "na", "n/a", "none", "unknown"):
+        return None
+    if v in ("female", "f", "woman", "girl", "famale"):
+        return 0
+    if v in ("male", "m", "man", "boy"):
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Initialize linked_data for completeness
+linked_data = None
+
+# 2-6. Proceed only if trait/clinical data are available; otherwise, skip linking and record unusable
+if 'trait_row' in globals() and (trait_row is not None):
+    # Ensure selected_clinical_data exists; if not, recreate it
+    if 'selected_clinical_data' in globals():
+        selected_clinical_df = selected_clinical_data
+    else:
+        selected_clinical_df = geo_select_clinical_features(
+            clinical_df=clinical_data,
+            trait=trait,
+            trait_row=trait_row,
+            convert_trait=convert_trait,
+            age_row=age_row,
+            convert_age=convert_age if 'age_row' in globals() and age_row is not None else None,
+            gender_row=gender_row,
+            convert_gender=convert_gender if 'gender_row' in globals() and gender_row is not None else None
+        )
+
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias check and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and cohort info saving
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Proceeded with linking and preprocessing."
+    )
+
+    # Save linked dataset only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # No trait data available: skip linking and mark dataset as unavailable for association analysis
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=normalized_gene_data,
+        note="INFO: Trait not available in clinical annotations; skipped linking and downstream processing."
+    )

output/preprocess/Hepatitis/code/GSE97475.py

@@ -0,0 +1,323 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+cohort = "GSE97475"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hepatitis"
+in_cohort_dir = "../DATA/GEO/Hepatitis/GSE97475"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/GSE97475.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/GSE97475.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/GSE97475.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability (microarray mentioned in series design)
+is_gene_available = True
+
+# 2) Variable availability and converters
+
+def _extract_header(value: str) -> str:
+    if pd.isna(value):
+        return ''
+    s = str(value)
+    if ':' in s:
+        return s.split(':', 1)[0].strip().lower()
+    return s.strip().lower()
+
+def _extract_value(value: str):
+    if pd.isna(value):
+        return None
+    s = str(value).strip()
+    if s == '' or s.lower() in {'na', 'n/a', 'nan', 'none', 'null', 'unknown', 'not available'}:
+        return None
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    if s.lower() in {'na', 'n/a', 'nan', 'none', 'null', 'unknown', 'not available'}:
+        return None
+    return s
+
+def _unique_non_null_values(series_like) -> set:
+    vals = []
+    for v in series_like:
+        vv = _extract_value(v)
+        if vv is not None:
+            vals.append(vv)
+    return set(vals)
+
+# Default to None; we will try to detect from clinical_data if possible
+trait_row = None
+age_row = None
+gender_row = None
+
+# Try to detect variable rows from the provided clinical_data DataFrame
+try:
+    # clinical_data is assumed to be present in the environment
+    row_headers = {}
+    unique_values_map = {}
+    for rid in clinical_data.index:
+        row_vals = clinical_data.loc[rid].tolist()
+        header = ''
+        # find the first non-empty header
+        for v in row_vals:
+            h = _extract_header(v)
+            if h:
+                header = h
+                break
+        row_headers[rid] = header
+        unique_values_map[rid] = _unique_non_null_values(row_vals)
+
+    # Detect age row: prioritize demographics age with more than one unique numeric value
+    age_candidates = []
+    for rid, header in row_headers.items():
+        if 'age' in header and 'demographics' in header:
+            uniq = unique_values_map[rid]
+            # count numeric-like values
+            numeric_count = 0
+            for u in uniq:
+                try:
+                    float(str(u).strip().replace('years', '').replace('year', '').replace('yrs', '').replace('yr', ''))
+                    numeric_count += 1
+                except Exception:
+                    pass
+            if len(uniq) > 1 and numeric_count >= 2:
+                age_candidates.append((rid, len(uniq), numeric_count))
+    if age_candidates:
+        # choose the row with the largest number of unique numeric values
+        age_candidates.sort(key=lambda x: (x[2], x[1]), reverse=True)
+        age_row = age_candidates[0][0]
+    else:
+        # Fallback: any header containing 'age' with >1 unique numeric values
+        fallback_age = []
+        for rid, header in row_headers.items():
+            if 'age' in header:
+                uniq = unique_values_map[rid]
+                numeric_count = 0
+                for u in uniq:
+                    try:
+                        float(str(u).strip().replace('years', '').replace('year', '').replace('yrs', '').replace('yr', ''))
+                        numeric_count += 1
+                    except Exception:
+                        pass
+                if len(uniq) > 1 and numeric_count >= 2:
+                    fallback_age.append((rid, len(uniq), numeric_count))
+        if fallback_age:
+            fallback_age.sort(key=lambda x: (x[2], x[1]), reverse=True)
+            age_row = fallback_age[0][0]
+
+    # Detect gender row: headers containing 'gender' or 'sex' with at least two unique values
+    gender_candidates = []
+    for rid, header in row_headers.items():
+        if ('gender' in header) or (re.search(r'\bsex\b', header) is not None):
+            uniq = {str(u).strip().lower() for u in unique_values_map[rid]}
+            # typical values include male/female; ensure >1 unique categorical values
+            if len(uniq) > 1:
+                gender_candidates.append((rid, len(uniq)))
+    if gender_candidates:
+        gender_candidates.sort(key=lambda x: x[1], reverse=True)
+        gender_row = gender_candidates[0][0]
+
+    # Detect trait row for Hepatitis: look for hepatitis/hbv/vaccine terms with >1 unique values
+    trait_candidates = []
+    hep_terms = ['hepatitis', 'hbv', 'hep b', 'hep-b', 'hepb', 'b vaccine', 'vaccin']
+    for rid, header in row_headers.items():
+        if any(t in header for t in hep_terms):
+            uniq = {str(u).strip().lower() for u in unique_values_map[rid]}
+            # if all values are same or empty, skip
+            if len(uniq) > 1:
+                trait_candidates.append((rid, len(uniq)))
+    # In this dataset (healthy HBV vaccine recipients), trait likely constant; only select if >1 unique
+    if trait_candidates:
+        trait_candidates.sort(key=lambda x: x[1], reverse=True)
+        trait_row = trait_candidates[0][0]
+    else:
+        trait_row = None
+
+except NameError:
+    # clinical_data not available in scope; fall back to known keys from the sample dictionary
+    # From provided snippet, age is at key 81; trait and gender likely unavailable/constant in this cohort
+    age_row = 81
+    trait_row = None
+    gender_row = None
+
+# Converters
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = str(v).lower().replace('years', '').replace('year', '').replace('yrs', '').replace('yr', '').strip()
+    # remove any trailing '+' or other non-numeric chars
+    s = re.sub(r'[^\d\.]+', '', s)
+    if s == '':
+        return None
+    try:
+        val = float(s)
+        # return integer if it is an integer value
+        return int(val) if abs(val - int(val)) < 1e-9 else val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    # common mappings
+    if s in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if s in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    # sometimes encoded as 0/1 or 1/2
+    if s in {'0'}:
+        # ambiguous; assume 0=female in our convention only if explicitly stated; otherwise None
+        return None
+    if s in {'1'}:
+        return None
+    if s in {'2'}:
+        # some datasets: 1=male,2=female
+        return 0
+    return None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    # Map presence of Hepatitis (disease) to 1; controls (including vaccinated healthy) to 0
+    positive_terms = {'hepatitis', 'hepatitis b', 'hbv', 'hbv-infected', 'chb', 'chronic hepatitis b', 'acute hepatitis b'}
+    negative_terms = {'healthy', 'control', 'non-hepatitis', 'vaccinated', 'vaccine recipient', 'hbv-negative', 'uninfected'}
+    if s in positive_terms:
+        return 1
+    if s in negative_terms:
+        return 0
+    # heuristics
+    if 'hepatitis' in s or re.search(r'\bhbv\b', s):
+        # If mentions infection/positive
+        if any(t in s for t in ['infect', 'positive', 'pos', 'carrier', 'hbsag+']):
+            return 1
+        if any(t in s for t in ['negative', 'neg', 'uninfect', 'vaccine', 'vaccinated', 'recipient']):
+            return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False,
+                                  cohort=cohort,
+                                  info_path=json_path,
+                                  is_gene_available=is_gene_available,
+                                  is_trait_available=is_trait_available)
+
+# 4) Clinical feature extraction (only if trait is available)
+if is_trait_available:
+    # Use detected age_row and gender_row if available; otherwise pass None
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine if clinical features were extracted previously
+has_selected_clinical = (
+    'selected_clinical_data' in globals()
+    and isinstance(selected_clinical_data, pd.DataFrame)
+    and not selected_clinical_data.empty
+)
+
+if has_selected_clinical:
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias assessment
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    note = "INFO: Clinical trait available; proceeding with standard preprocessing."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait not available; perform final validation without linking and do not save linked data
+    note = ("INFO: Trait not available; cohort consists of healthy HBV vaccine recipients with no "
+            "case-control variation for Hepatitis. Skipping linking and downstream steps.")
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=normalized_gene_data,
+        note=note
+    )
+    # Do not save out_data_file when trait is unavailable

output/preprocess/Hepatitis/code/TCGA.py

@@ -0,0 +1,289 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hepatitis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hepatitis/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Hepatitis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hepatitis/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Hepatitis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA cohort directory for the trait "Hepatitis"
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Keyword-based scoring to find best match
+keywords_priority = {
+    "hepatitis": 3,
+    "hepatocellular": 2,
+    "liver": 1
+}
+
+best_dir = None
+best_score = -1
+for d in subdirs:
+    name_lower = d.lower()
+    score = sum(w for k, w in keywords_priority.items() if k in name_lower)
+    if score > best_score:
+        best_score = score
+        best_dir = d
+
+# If no suitable directory is found, mark as unavailable and exit early
+if best_dir is None or best_score <= 0:
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort directory found for the trait. Skipping.")
+else:
+    selected_cohort_dir = os.path.join(tcga_root_dir, best_dir)
+    print(f"Selected cohort directory: {selected_cohort_dir}")
+
+    # Step 2: Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(selected_cohort_dir)
+    print(f"Clinical file: {clinical_file_path}")
+    print(f"Genetic file: {genetic_file_path}")
+
+    # Step 3: Load both files as DataFrames
+    def _read_tcga_file(fp: str) -> pd.DataFrame:
+        compression = 'gzip' if fp.lower().endswith('.gz') else 'infer'
+        return pd.read_csv(fp, sep='\t', index_col=0, low_memory=False, compression=compression)
+
+    clinical_df = _read_tcga_file(clinical_file_path)
+    genetic_df = _read_tcga_file(genetic_file_path)
+
+    # Step 4: Print the column names of the clinical data
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import re
+import pandas as pd
+
+# Determine cohort directory and clinical file path
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Liver_Cancer_(LIHC)")
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load clinical data
+clinical_df = pd.read_csv(clinical_file_path, sep="\t", index_col=0, dtype=str)
+
+# Refined regex patterns to avoid substring false positives (e.g., 'stage')
+pattern_age = re.compile(r'(^|[^a-zA-Z])age([^a-zA-Z]|$)|birth', re.I)
+pattern_gender = re.compile(r'(^|[^a-zA-Z])gender([^a-zA-Z]|$)|(^|[^a-zA-Z])sex([^a-zA-Z]|$)', re.I)
+
+# Exclude known confounders
+exclude_age = {"pathologic_stage"}
+
+# Identify candidate columns in original order
+candidate_age_cols = [c for c in clinical_df.columns if pattern_age.search(c) and c not in exclude_age]
+candidate_gender_cols = [c for c in clinical_df.columns if pattern_gender.search(c)]
+
+# Print required lists
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Preview extracted data
+if candidate_age_cols:
+    age_preview = preview_df(clinical_df[candidate_age_cols], n=5)
+    print(age_preview)
+if candidate_gender_cols:
+    gender_preview = preview_df(clinical_df[candidate_gender_cols], n=5)
+    print(gender_preview)
+
+# Step 3: Select Demographic Features
+# Select demographic feature columns based on data validity and completeness
+
+age_col = None
+gender_col = None
+
+def _age_valid_rate(series):
+    # Convert to numeric ages and assess plausibility (0-120 years)
+    ages = series.apply(tcga_convert_age)
+    if ages is None or len(ages) == 0:
+        return 0.0, 0.0
+    valid_mask = ages.apply(lambda x: (x is not None) and (0 <= x <= 120))
+    valid_rate = valid_mask.mean()
+    nonnull_rate = series.notna().mean()
+    return float(valid_rate), float(nonnull_rate)
+
+def _gender_valid_rate(series):
+    genders = series.apply(tcga_convert_gender)
+    if genders is None or len(genders) == 0:
+        return 0.0
+    return float(genders.notna().mean())
+
+# Try data-driven selection if clinical_df is available
+if 'clinical_df' in globals():
+    # Age: select column with highest valid rate (0-120) and then highest non-null rate
+    best_age = None
+    best_age_score = (-1.0, -1.0)  # (valid_rate, nonnull_rate)
+    for c in candidate_age_cols:
+        if c in clinical_df.columns:
+            vr, nr = _age_valid_rate(clinical_df[c])
+            # Prefer columns with "age" in name if scores tie
+            prefer = 1 if ("age" in c.lower()) else 0
+            score = (vr, nr, prefer)
+            if (score > (best_age_score[0], best_age_score[1], 0)):
+                best_age = c
+                best_age_score = (vr, nr)
+    # Ensure reasonable validity threshold
+    if best_age is not None and best_age_score[0] >= 0.5:
+        age_col = best_age
+    else:
+        age_col = None
+
+    # Gender: select column with highest valid rate (recognized by tcga_convert_gender)
+    best_gender = None
+    best_gender_rate = -1.0
+    for c in candidate_gender_cols:
+        if c in clinical_df.columns:
+            vr = _gender_valid_rate(clinical_df[c])
+            if vr > best_gender_rate:
+                best_gender = c
+                best_gender_rate = vr
+    if best_gender is not None and best_gender_rate >= 0.5:
+        gender_col = best_gender
+    else:
+        gender_col = None
+else:
+    # Fallback: heuristic based on column names if clinical_df is not available
+    for c in candidate_age_cols:
+        if "age" in c.lower():
+            age_col = c
+            break
+    if age_col is None and len(candidate_age_cols) > 0:
+        age_col = candidate_age_cols[0]
+
+    for c in candidate_gender_cols:
+        if "gender" in c.lower() or "sex" in c.lower():
+            gender_col = c
+            break
+    if gender_col is None and len(candidate_gender_cols) > 0:
+        gender_col = candidate_gender_cols[0]
+
+# Explicitly print out the information for the chosen columns
+print(f"Selected age_col: {age_col}")
+if age_col is not None and 'clinical_df' in globals() and age_col in clinical_df.columns:
+    print("age_col preview (first 5):", clinical_df[age_col].head(5).tolist())
+    vr, nr = _age_valid_rate(clinical_df[age_col])
+    print(f"age_col valid_rate (0-120 years): {vr:.3f}, nonnull_rate: {nr:.3f}")
+
+print(f"Selected gender_col: {gender_col}")
+if gender_col is not None and 'clinical_df' in globals() and gender_col in clinical_df.columns:
+    print("gender_col preview (first 5):", clinical_df[gender_col].head(5).tolist())
+    gr = _gender_valid_rate(clinical_df[gender_col])
+    nnr = clinical_df[gender_col].notna().mean() if 'clinical_df' in globals() else None
+    print(f"gender_col valid_rate (recognized male/female): {gr:.3f}, nonnull_rate: {nnr:.3f}" if nnr is not None else f"gender_col valid_rate: {gr:.3f}")
+
+# Step 4: Feature Engineering and Validation
+import os
+import re
+import pandas as pd
+
+# Ensure cohort data is loaded (reuse from previous steps if available; otherwise reload)
+if 'clinical_df' not in globals() or 'genetic_df' not in globals():
+    cohort_dir = os.path.join(tcga_root_dir, "TCGA_Liver_Cancer_(LIHC)")
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    def _read_tcga_file(fp: str) -> pd.DataFrame:
+        compression = 'gzip' if fp.lower().endswith('.gz') else 'infer'
+        return pd.read_csv(fp, sep='\t', index_col=0, low_memory=False, compression=compression)
+
+    clinical_df = _read_tcga_file(clinical_file_path)
+    genetic_df = _read_tcga_file(genetic_file_path)
+
+# 1) Extract and standardize clinical features (Age, Gender) and construct Hepatitis trait from viral_hepatitis_serology
+age_arg = age_col if ('age_col' in globals() and age_col in clinical_df.columns) else None
+gender_arg = gender_col if ('gender_col' in globals() and gender_col in clinical_df.columns) else None
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait=trait, age_col=age_arg, gender_col=gender_arg)
+
+def _convert_hepatitis(x):
+    s = str(x).strip().lower()
+    if s in ("", "nan", "none", "not available", "na", "n/a", "unknown", "not reported", "null", "missing"):
+        return None
+    # Normalize spaces
+    s = re.sub(r"\s+", " ", s)
+    # Common synonyms
+    if any(tok in s for tok in ["positive", "pos", "yes", "y", "reactive", "detected", "presence"]):
+        # Avoid false positive from "non-reactive"/"not detected"
+        if any(tok in s for tok in ["nonreactive", "non-reactive", "not detected", "negative", "neg", "no", "n"]):
+            # conflicting signals; set None
+            return None
+        return 1
+    if any(tok in s for tok in ["negative", "neg", "no", "n", "nonreactive", "non-reactive", "not detected", "absence"]):
+        return 0
+    if s in {"1", "true"}:
+        return 1
+    if s in {"0", "false"}:
+        return 0
+    return None
+
+if "viral_hepatitis_serology" in clinical_df.columns:
+    selected_clinical_df[trait] = clinical_df["viral_hepatitis_serology"].apply(_convert_hepatitis)
+else:
+    # Trait not available; record and stop after saving gene data
+    trait_available = False
+
+# Determine trait availability (at least one non-missing label)
+trait_available = bool(selected_clinical_df[trait].notna().sum() > 0) if "viral_hepatitis_serology" in clinical_df.columns else False
+
+# 2) Normalize gene symbols and save
+gene_df = genetic_df.copy()
+gene_df_norm = normalize_gene_symbols_in_index(gene_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_df_norm.to_csv(out_gene_data_file)
+
+# If the trait is unavailable, record and stop early (do not link or save cohort data)
+if not trait_available:
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    # 3) Link clinical and genetic data on sample IDs
+    gene_df_norm_T = gene_df_norm.T  # samples x genes
+    linked_data = selected_clinical_df.join(gene_df_norm_T, how='inner')
+
+    # 4) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 5) Judge and remove biased features; record trait bias for final validation
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+
+    # 6) Final validation and save cohort info
+    note = (
+        "INFO: Cohort LIHC; Trait 'Hepatitis' derived from clinical column 'viral_hepatitis_serology': "
+        "positive/reactive/detected/yes -> 1; negative/nonreactive/not detected/no -> 0; others -> NaN. "
+        f"Age from '{age_arg}' and Gender from '{gender_arg}'. "
+        "Gene symbols normalized using NCBI synonym mapping; samples inner-joined across clinical and expression data."
+    )
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=trait_available,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=note
+    )
+
+    # 7) Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

output/preprocess/Hepatitis/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE97475": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 158
-  },
-  "GSE85550": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE66843": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 17
-  },
-  "GSE45032": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 48
-  },
-  "GSE168049": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 16
-  },
-  "GSE159676": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 33
-  },
-  "GSE152738": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE125860": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE124719": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE114783": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 423
-  }
-}
+{"GSE97475": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait not available; cohort consists of healthy HBV vaccine recipients with no case-control variation for Hepatitis. Skipping linking and downstream steps."}, "GSE85550": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait not available in clinical annotations; skipped linking and downstream processing."}, "GSE66843": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 17, "note": "INFO: Cell-line HCV infection model (Huh7.5.1). Trait derived from infection status; no Age/Gender available. ILMN probes mapped via 'Symbol' and gene symbols normalized."}, "GSE45032": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 48, "note": "INFO: Trait encoding for Hepatitis: CHC=1 (chronic hepatitis C), HCC=0 (hepatocellular carcinoma)."}, "GSE168049": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE159676": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 33, "note": ""}, "GSE152738": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE125860": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 172, "note": "INFO: Probe-to-gene mapping applied and gene symbols normalized in Step 6; age and gender not available in this series; trait is continuous HBV post-vaccination antibody concentration (mIU/mL) with left-censored values (e.g., '<5') mapped to the threshold."}, "GSE124719": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 179, "note": "INFO: All participants are male; Gender not available. Trait defined as HepB vaccine (FENDRIX/FENDRIXE) vs others."}, "GSE114783": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 36, "note": "INFO: Gene symbol normalization yielded low retention (ratio=0.0000); retained original identifiers (likely Entrez IDs)."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 22, "note": "INFO: Cohort LIHC; Trait 'Hepatitis' derived from clinical column 'viral_hepatitis_serology': positive/reactive/detected/yes -> 1; negative/nonreactive/not detected/no -> 0; others -> NaN. Age from 'age_at_initial_pathologic_diagnosis' and Gender from 'gender'. Gene symbols normalized using NCBI synonym mapping; samples inner-joined across clinical and expression data."}}

output/preprocess/High-Density_Lipoprotein_Deficiency/code/GSE34945.py

@@ -0,0 +1,130 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "High-Density_Lipoprotein_Deficiency"
+cohort = "GSE34945"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/High-Density_Lipoprotein_Deficiency"
+in_cohort_dir = "../DATA/GEO/High-Density_Lipoprotein_Deficiency/GSE34945"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/GSE34945.csv"
+out_gene_data_file = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/gene_data/GSE34945.csv"
+out_clinical_data_file = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/clinical_data/GSE34945.csv"
+json_path = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Decide data availability based on provided background/sample characteristics
+is_gene_available = False  # SNP genotyping only; no gene expression matrix suitable for analysis here.
+
+# No explicit or inferable human-level trait (HDL deficiency), age, or gender fields in the sample characteristics.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Converters
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    val = val.strip()
+    return val if val != "" else None
+
+def convert_trait(x):
+    # Binary: 1 = High-Density Lipoprotein Deficiency present; 0 = absent
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Map common indicators of HDL deficiency
+    keywords_pos = [
+        "hdl deficiency", "high-density lipoprotein deficiency", "hypoalphalipoproteinemia",
+        "low hdl", "reduced hdl", "decreased hdl"
+    ]
+    keywords_neg = ["normal hdl", "control"]
+    if any(k in v for k in keywords_pos):
+        return 1
+    if any(k in v for k in keywords_neg):
+        return 0
+    # For this dataset, entries like "mixed dyslipidemia", "treatment group", or "percent change in apoc3" are not trait labels
+    return None
+
+def convert_age(x):
+    # Continuous age in years
+    val = _extract_value(x)
+    if val is None:
+        return None
+    import re
+    # Extract first integer/float number
+    m = re.search(r"(-?\d+\.?\d*)", val)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    # Handle numeric encodings if present
+    if v in {"0", "1"}:
+        return int(v)
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction: skip because trait_row is None (no usable clinical trait data here)
+# If in another scenario trait_row is not None, the following would be used:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df, n=5)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/High-Density_Lipoprotein_Deficiency/code/TCGA.py

@@ -0,0 +1,72 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "High-Density_Lipoprotein_Deficiency"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/High-Density_Lipoprotein_Deficiency/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import re
+import pandas as pd
+
+# Helper to normalize names for matching
+def _normalize(text: str) -> str:
+    return re.sub(r'[^a-z0-9]+', ' ', text.lower()).strip()
+
+# Keywords related to High-Density Lipoprotein Deficiency
+trait_keywords = [
+    'hdl', 'high density lipoprotein', 'high density', 'high-density',
+    'lipoprotein', 'cholesterol', 'hdl c', 'hdl-c',
+    'apoa1', 'apolipoprotein a1', 'abca1', 'tangier', 'hypoalphalipoproteinemia'
+]
+trait_keywords_norm = [_normalize(k) for k in trait_keywords]
+
+# List subdirectories and attempt to find best-matching cohort
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+scored = []
+for d in subdirs:
+    d_norm = _normalize(d)
+    hits = sum(1 for k in trait_keywords_norm if k and k in d_norm)
+    if hits > 0:
+        scored.append((hits, len(d_norm), d))
+
+selected_tcga_dir = None
+if scored:
+    # Most specific = most hits; tie-breaker = shorter name length
+    scored.sort(key=lambda x: (-x[0], x[1]))
+    selected_tcga_dir = scored[0][2]
+
+clinical_df = None
+genetic_df = None
+
+if selected_tcga_dir is None:
+    # No suitable cohort; record and exit this step gracefully
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA_no_matching_cohort",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_tcga_dir)
+
+    # Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Load dataframes (handle possible gzip with compression='infer')
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Print clinical columns for further analysis
+    print(clinical_df.columns.tolist())

output/preprocess/High-Density_Lipoprotein_Deficiency/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE34945": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": true,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE34945": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA_no_matching_cohort": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Huntingtons_Disease/clinical_data/GSE26927.csv

@@ -1,4 +1,4 @@
-,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
-Huntingtons_Disease,0.0,0.0,1.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,
-Age,70.0,73.0,59.0,40.0,47.0,82.0,86.0,93.0,72.0,85.0,80.0,79.0,76.0,77.0,55.0,43.0,39.0,67.0,84.0,54.0,74.0,69.0,64.0,60.0,68.0,18.0,57.0,46.0,50.0,53.0
-Gender,1.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM663008,GSM663009,GSM663010,GSM663011,GSM663012,GSM663013,GSM663014,GSM663015,GSM663016,GSM663017,GSM663018,GSM663019,GSM663020,GSM663021,GSM663022,GSM663023,GSM663024,GSM663025,GSM663026,GSM663027,GSM663028,GSM663029,GSM663030,GSM663031,GSM663032,GSM663033,GSM663034,GSM663035,GSM663036,GSM663037,GSM663038,GSM663039,GSM663040,GSM663041,GSM663042,GSM663043,GSM663044,GSM663045,GSM663046,GSM663047,GSM663048,GSM663049,GSM663050,GSM663051,GSM663052,GSM663053,GSM663054,GSM663055,GSM663056,GSM663057,GSM663058,GSM663059,GSM663060,GSM663061,GSM663062,GSM663063,GSM663064,GSM663065,GSM663066,GSM663067,GSM663068,GSM663069,GSM663070,GSM663071,GSM663072,GSM663073,GSM663074,GSM663075,GSM663076,GSM663077,GSM663078,GSM663079,GSM663080,GSM663081,GSM663082,GSM663083,GSM663084,GSM663085,GSM663086,GSM663087,GSM663088,GSM663089,GSM663090,GSM663091,GSM663092,GSM663093,GSM663094,GSM663095,GSM663096,GSM663097,GSM663098,GSM663099,GSM663100,GSM663101,GSM663102,GSM663103,GSM663104,GSM663105,GSM663106,GSM663107,GSM663108,GSM663109,GSM663110,GSM663111,GSM663112,GSM663113,GSM663114,GSM663115,GSM663116,GSM663117,GSM663118,GSM663119,GSM663120,GSM663121,GSM663122,GSM663123,GSM663124,GSM663125
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+Age,70.0,73.0,73.0,59.0,40.0,47.0,47.0,82.0,86.0,82.0,93.0,82.0,72.0,85.0,80.0,79.0,76.0,77.0,55.0,55.0,43.0,39.0,77.0,67.0,84.0,84.0,82.0,82.0,54.0,72.0,82.0,74.0,69.0,69.0,74.0,64.0,60.0,64.0,64.0,60.0,68.0,18.0,57.0,46.0,50.0,46.0,53.0,75.0,51.0,38.0,74.0,57.0,54.0,72.0,57.0,60.0,,69.0,59.0,47.0,56.0,53.0,55.0,57.0,46.0,50.0,53.0,55.0,51.0,53.0,53.0,42.0,53.0,45.0,53.0,45.0,45.0,54.0,66.0,54.0,64.0,55.0,55.0,60.0,58.0,104.0,86.0,78.0,85.0,76.0,77.0,80.0,80.0,80.0,86.0,87.0,81.0,82.0,41.0,91.0,57.0,53.0,63.0,66.0,79.0,57.0,50.0,55.0,51.0,64.0,64.0,73.0,43.0,77.0,76.0,63.0,81.0,71.0
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output/preprocess/Huntingtons_Disease/clinical_data/GSE34721.csv

@@ -1,3 +1,3 @@
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output/preprocess/Huntingtons_Disease/code/GSE135589.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+cohort = "GSE135589"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
+in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE135589"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/GSE135589.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/GSE135589.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/GSE135589.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+import numpy as np
+
+# 1) Gene expression data availability (based on background info: peripheral blood RNA gene expression)
+is_gene_available = True
+
+# 2) Variable availability (identify rows from the provided Sample Characteristics Dictionary)
+trait_row = 4    # 'disease stage: Control / preHD A/B / zHD stage1/2'
+age_row = 2      # 'age at year 1: <number>'
+gender_row = 1   # 'Sex: Male/Female'
+
+# 2.2) Conversion functions
+def _after_colon(x: str) -> str:
+    if x is None or (isinstance(x, float) and np.isnan(x)):
+        return ''
+    parts = str(x).split(':', 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    v = _after_colon(x).lower()
+    if v == '' or v in {'na', 'n/a', 'none', 'unknown'}:
+        return None
+    # Map to Huntington's disease status: control=0, preHD/zHD=1
+    if 'control' in v:
+        return 0
+    if 'prehd' in v or 'zhd' in v or 'stage' in v:
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(x).lower()
+    if v == '' or v in {'na', 'n/a', 'none', 'unknown'}:
+        return None
+    m = re.search(r'[-+]?\d+(\.\d+)?', v)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x).lower()
+    if 'female' in v:
+        return 0
+    if 'male' in v:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Decide the identifier and gene symbol columns from the gene annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2. Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    selected_clinical_df
+except NameError:
+    # Fallback: load clinical data from file if not in scope
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_final = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in unbiased_linked_data.columns)
+
+note = "INFO: Trait coded as Control=0 and preHD/zHD=1. Age corresponds to 'age at year 1'."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Huntingtons_Disease/code/GSE154141.py

@@ -0,0 +1,507 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+cohort = "GSE154141"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
+in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE154141"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/GSE154141.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/GSE154141.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/GSE154141.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Lentiviral manipulation with Q-lengths strongly suggests gene expression profiling.
+
+# 2) Variable availability and converters
+# From the sample characteristics dictionary:
+# 0: sort: Gpos_Pneg / Gpos_Ppos  -> sorting status, not the trait
+# 1: lentivirus: pTANK / Q23 / Q73 -> disease modeling; use as trait
+# 2: sampleID: A/B/C               -> IDs
+trait_row = 1
+age_row = None      # No age field in the characteristics; unavailable.
+gender_row = None   # No gender field in the characteristics; unavailable.
+
+def _value_after_colon(x):
+    if x is None:
+        return None
+    try:
+        parts = str(x).split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        val = val.strip()
+        return val if val != "" else None
+    except Exception:
+        return None
+
+def convert_trait(x):
+    v = _value_after_colon(x)
+    if v is None:
+        return None
+    s = re.sub(r"\s+", "", str(v)).lower()
+    # Map common controls
+    if s in {"ptank", "control", "ctrl", "vehicle", "empty", "mock"}:
+        return 0
+    # Map Q-lengths by threshold for HD
+    m = re.search(r"q(\d+)", s)
+    if m:
+        try:
+            q = int(m.group(1))
+            return 1 if q >= 36 else 0
+        except Exception:
+            pass
+    # Heuristics
+    if any(t in s for t in ["mut", "expanded", "hd", "mhtt", "htt-exp"]):
+        return 1
+    if any(t in s for t in ["wt", "nonhd", "normal", "unchanged"]):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    m = re.search(r"(\d+(\.\d+)?)", s)
+    if not m:
+        return None
+    try:
+        num = float(m.group(1))
+    except Exception:
+        return None
+    if "month" in s:
+        return num / 12.0
+    if "day" in s or "d " in s:
+        return num / 365.0
+    if "week" in s:
+        return num / 52.0
+    return num
+
+def convert_gender(x):
+    v = _value_after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"female", "f", "woman", "girl"}:
+        return 0
+    if s in {"male", "m", "man", "boy"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical)
+    print(preview)
+    # Save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import re
+import pandas as pd
+
+# Prepare expression probe ID set
+expr_ids = set(gene_data.index.astype(str).str.strip())
+
+# Collect all SOFT files from cohort dir and trait dir (to capture GPL files if present)
+soft_files = []
+for base in [in_cohort_dir, in_trait_dir]:
+    for root, _, files in os.walk(base):
+        for f in files:
+            if 'soft' in f.lower():
+                soft_files.append(os.path.join(root, f))
+soft_files = list(dict.fromkeys(soft_files))  # de-duplicate while preserving order
+
+def pick_best_annotation(soft_paths, expr_ids_set):
+    best = {"soft_path": None, "id_col": None, "overlap": -1, "annotation": None}
+    diagnostics = []
+    for sp in soft_paths:
+        try:
+            ann = get_gene_annotation(sp)
+        except Exception as e:
+            diagnostics.append((os.path.basename(sp), "READ_FAIL", 0, str(e)))
+            continue
+
+        # Evaluate overlap for each column
+        top_col = None
+        top_overlap = -1
+        for col in ann.columns:
+            try:
+                series = ann[col].astype(str).str.strip()
+            except Exception:
+                continue
+            # Drop obvious non-ID columns by limiting value length (heuristic)
+            vals = series.dropna().unique()
+            if len(vals) == 0:
+                continue
+            # Compute overlap
+            overlap = len(expr_ids_set.intersection(set(vals)))
+            if overlap > top_overlap:
+                top_overlap = overlap
+                top_col = col
+
+        diagnostics.append((os.path.basename(sp), top_col, top_overlap, None))
+        if top_overlap > best["overlap"]:
+            best.update({"soft_path": sp, "id_col": top_col, "overlap": top_overlap, "annotation": ann})
+
+    print("Annotation matching diagnostics (file, chosen_id_col, overlap):")
+    for d in diagnostics:
+        print("  ", d)
+    return best
+
+best_match = pick_best_annotation(soft_files, expr_ids)
+
+# Require a minimal meaningful overlap
+min_required_overlap = 100
+if best_match["overlap"] < min_required_overlap or best_match["annotation"] is None or best_match["id_col"] is None:
+    raise RuntimeError(
+        f"No suitable annotation found for expression IDs. Best overlap={best_match['overlap']} "
+        f"with file={os.path.basename(best_match['soft_path']) if best_match['soft_path'] else None} "
+        f"and column={best_match['id_col']}. Please verify platform files in {in_cohort_dir}."
+    )
+
+gene_annotation_all = best_match["annotation"]
+probe_col = best_match["id_col"]
+
+# Subset annotation to rows matching our expression probes
+ann_ids_series = gene_annotation_all[probe_col].astype(str).str.strip()
+ann_subset = gene_annotation_all.loc[ann_ids_series.isin(expr_ids)].copy()
+
+# Identify the best gene symbol column within the subset
+def find_gene_symbol_col_in_subset(df_subset: pd.DataFrame) -> str | None:
+    preferred = [
+        "Gene Symbol", "GENE_SYMBOL", "Gene symbol", "Symbol", "SYMBOL", "GeneSymbol",
+        "Approved Symbol", "Associated Gene Name", "Associated.Symbol", "gene_assignment"
+    ]
+    # Exact preferred matches first (case-insensitive), prioritizing non-null count
+    candidates = []
+    for p in preferred:
+        for c in df_subset.columns:
+            if c.lower() == p.lower():
+                nonnull = df_subset[c].notna().sum()
+                if nonnull > 0:
+                    candidates.append((c, nonnull))
+    if candidates:
+        candidates.sort(key=lambda x: x[1], reverse=True)
+        return candidates[0][0]
+    # Heuristic: any column name containing 'symbol' with non-null values
+    symbol_like = []
+    for c in df_subset.columns:
+        if "symbol" in c.lower():
+            nonnull = df_subset[c].notna().sum()
+            if nonnull > 0:
+                symbol_like.append((c, nonnull))
+    if symbol_like:
+        symbol_like.sort(key=lambda x: x[1], reverse=True)
+        return symbol_like[0][0]
+    return None
+
+gene_symbol_col = find_gene_symbol_col_in_subset(ann_subset)
+
+# Fallback: parse gene symbol from 'Target Description' if symbol column unavailable or mostly empty
+def parse_symbol_from_target_desc(s: str) -> str | None:
+    if not isinstance(s, str):
+        return None
+    # Try /UG_GENE= or /GEN= tags
+    m = re.search(r"/UG_GENE=([A-Za-z0-9\-]+)", s)
+    if not m:
+        m = re.search(r"/GEN=([A-Za-z0-9\-]+)", s)
+    if m:
+        return m.group(1)
+    return None
+
+if gene_symbol_col is None or ann_subset[gene_symbol_col].notna().sum() == 0:
+    if "Target Description" in ann_subset.columns:
+        ann_subset["__ParsedSymbol__"] = ann_subset["Target Description"].apply(parse_symbol_from_target_desc)
+        if ann_subset["__ParsedSymbol__"].notna().sum() == 0:
+            # As a last heuristic, try to extract any all-caps token up to 10 chars as a putative symbol
+            def loose_parse(s: str) -> str | None:
+                if not isinstance(s, str):
+                    return None
+                toks = re.findall(r"\b[A-Z][A-Z0-9\-]{1,9}\b", s)
+                if len(toks) > 0:
+                    return toks[0]
+                return None
+            ann_subset["__ParsedSymbol__"] = ann_subset["Target Description"].apply(loose_parse)
+        gene_symbol_col = "__ParsedSymbol__"
+    else:
+        raise RuntimeError("No gene symbol column found and 'Target Description' absent for fallback parsing.")
+
+# Build mapping dataframe from subset
+mapping_df = ann_subset[[probe_col, gene_symbol_col]].rename(columns={probe_col: "ID", gene_symbol_col: "Gene"})
+mapping_df = mapping_df.dropna(subset=["ID", "Gene"]).copy()
+
+# Ensure mapping IDs align with expression probes
+mapping_df["ID"] = mapping_df["ID"].astype(str).str.strip()
+mapping_df = mapping_df[mapping_df["ID"].isin(gene_data.index)]
+
+# Normalize gene symbols to uppercase to survive human-gene extractor
+mapping_df["Gene"] = mapping_df["Gene"].astype(str).str.strip().str.upper()
+
+# Drop empty gene symbols after cleanup
+mapping_df = mapping_df[mapping_df["Gene"] != ""]
+
+# Apply mapping to convert probe-level data to gene-level expression
+mapped_gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Diagnostics
+n_probes = len(expr_ids)
+n_mapping_ids = mapping_df['ID'].nunique()
+n_genes = mapped_gene_data.shape[0]
+print(f"Mapping diagnostics: n_probes_in_expr={n_probes}, n_probes_mapped={n_mapping_ids}, n_genes_result={n_genes}")
+
+if n_genes == 0:
+    raise RuntimeError("Gene mapping resulted in zero genes after refined selection. Likely platform mismatch or unusable annotation.")
+
+# Overwrite gene_data with the mapped gene-level dataframe
+gene_data = mapped_gene_data
+
+# Step 7: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 8: Gene Identifier Mapping
+import gzip
+import io
+import re
+import pandas as pd
+
+# Use existing gene_data (probe-level expression) and soft_file from previous steps
+
+# 1) Parse all platform tables from the family SOFT and evaluate ID overlap with expression probes
+expr_ids = set(gene_data.index.astype(str).str.strip())
+
+def iter_platform_tables(soft_fp: str):
+    """Yield (platform_id, table_df) for each !platform_table block in the family SOFT."""
+    with gzip.open(soft_fp, 'rt') as f:
+        in_table = False
+        buf = []
+        platform_id = None
+        last_platform_id = None
+        for line in f:
+            line = line.rstrip('\n')
+            if line.startswith('!Platform_geo_accession') or line.startswith('!platform_geo_accession'):
+                parts = line.split('=', 1)
+                if len(parts) == 2:
+                    last_platform_id = parts[1].strip()
+            if line.startswith('^PLATFORM'):
+                parts = line.split('=', 1)
+                if len(parts) == 2:
+                    last_platform_id = parts[1].strip()
+            if line.startswith('!platform_table_begin') or line.startswith('!Platform_table_begin'):
+                in_table = True
+                buf = []
+                platform_id = last_platform_id
+                continue
+            if line.startswith('!platform_table_end') or line.startswith('!Platform_table_end'):
+                in_table = False
+                table_txt = '\n'.join(buf)
+                try:
+                    df = pd.read_csv(io.StringIO(table_txt), sep='\t', dtype=str, low_memory=False, on_bad_lines='skip')
+                    df.columns = [c.strip() for c in df.columns]
+                    yield (platform_id, df)
+                except Exception:
+                    pass
+                buf = []
+                platform_id = None
+                continue
+            if in_table:
+                buf.append(line)
+
+def best_id_match(df: pd.DataFrame, expr_ids: set):
+    candidates = [
+        "ID", "ID_REF",
+        "Array Address ID", "Array_Address_ID", "ArrayAddressID", "Array_Address_Id", "Array Address Id",
+        "Reporter Identifier", "Reporter_Name", "Reporter Name",
+        "ProbeID", "Probe Id", "PROBE_ID", "TargetID", "TARGETID",
+        "Representative Public ID", "GB_ACC", "ACCESSION"
+    ]
+    present = [c for c in candidates if c in df.columns]
+    if not present:
+        present = list(df.columns)
+    expr_ids_digits = set(re.sub(r"\D", "", x) for x in expr_ids if re.sub(r"\D", "", x) != "")
+    best = {"col": None, "norm": None, "overlap": -1}
+    for col in present:
+        series = df[col].astype(str).str.strip()
+        vals = set(series.dropna().unique())
+        ovA = len(expr_ids.intersection(vals))
+        vals_no_ilmn = set(re.sub(r"^ILMN_", "", v) for v in vals)
+        ovB = len(expr_ids.intersection(vals_no_ilmn))
+        vals_digits = set(re.sub(r"\D", "", v) for v in vals if re.sub(r"\D", "", v) != "")
+        ovC = len(expr_ids_digits.intersection(vals_digits))
+        for norm, ov in [("direct", ovA), ("drop_ilmn_prefix", ovB), ("digits_only", ovC)]:
+            if ov > best["overlap"]:
+                best.update({"col": col, "norm": norm, "overlap": ov})
+    return best
+
+# Scan all platform tables and pick the one with maximal overlap
+diagnostics = []
+best_block = {"platform": None, "df": None, "id_col": None, "norm": None, "overlap": -1}
+for plat_id, plat_df in iter_platform_tables(soft_file):
+    match = best_id_match(plat_df, expr_ids)
+    diagnostics.append((plat_id, match["col"], match["norm"], match["overlap"]))
+    if match["overlap"] > best_block["overlap"]:
+        best_block = {"platform": plat_id, "df": plat_df, "id_col": match["col"], "norm": match["norm"], "overlap": match["overlap"]}
+
+print("Platform matching diagnostics (platform, id_col, norm, overlap):")
+for d in diagnostics:
+    print("  ", d)
+
+if best_block["df"] is None or best_block["id_col"] is None or best_block["overlap"] < 100:
+    raise RuntimeError(f"No suitable platform table found to match expression probe IDs. Best overlap={best_block['overlap']} for platform={best_block['platform']} and id_col={best_block['id_col']}.")
+
+plat_df = best_block["df"]
+probe_col = best_block["id_col"]
+norm_strategy = best_block["norm"]
+
+# 2) Robust gene symbol/text column selection driven by extraction
+def pick_gene_text_col(df: pd.DataFrame, id_col: str) -> tuple[str, int]:
+    # Prefer common symbol/name columns if present
+    preferred = [
+        "Gene Symbol", "GENE_SYMBOL", "Gene symbol", "Symbol", "SYMBOL", "GeneSymbol",
+        "Gene", "GENE", "ILMN_Gene", "Associated Gene Name", "Associated.Symbol",
+        "gene_assignment", "Gene Title", "Target Description", "Description", "DEFINITION", "Definition", "DESCRIPTION"
+    ]
+    candidates = [c for c in preferred if c in df.columns and c != id_col]
+    if not candidates:
+        candidates = [c for c in df.columns if c != id_col]
+
+    best_col = None
+    best_score = -1
+
+    # Evaluate by how many rows yield at least one plausible human-like gene symbol after uppercasing
+    # Use a sample for efficiency if very large
+    eval_df = df
+    if len(df) > 10000:
+        eval_df = df.iloc[:10000].copy()
+
+    for c in candidates:
+        series = eval_df[c].dropna().astype(str).str.strip()
+        if series.empty:
+            continue
+        # Uppercase to make symbol extraction robust
+        series_upper = series.str.upper()
+        count = 0
+        for val in series_upper:
+            if len(extract_human_gene_symbols(val)) > 0:
+                count += 1
+        if count > best_score:
+            best_score = count
+            best_col = c
+        # Early exit if a clear symbol column found
+        if count > 1000:
+            break
+    return best_col, best_score
+
+gene_symbol_col, score = pick_gene_text_col(plat_df, probe_col)
+if gene_symbol_col is None or score <= 0:
+    raise RuntimeError("Unable to locate a gene symbol/text column that yields extractable symbols in the selected platform table.")
+
+# 3) Build mapping dataframe and apply mapping
+ann = plat_df[[probe_col, gene_symbol_col]].dropna(subset=[probe_col, gene_symbol_col]).copy()
+ann[probe_col] = ann[probe_col].astype(str).str.strip()
+
+if norm_strategy == "direct":
+    ann["ID"] = ann[probe_col]
+elif norm_strategy == "drop_ilmn_prefix":
+    ann["ID"] = ann[probe_col].str.replace(r"^ILMN_", "", regex=True)
+elif norm_strategy == "digits_only":
+    ann["ID"] = ann[probe_col].str.replace(r"\D", "", regex=True)
+else:
+    ann["ID"] = ann[probe_col]
+
+# Keep only IDs present in expression
+ann["ID"] = ann["ID"].astype(str).str.strip()
+ann = ann[ann["ID"].isin(expr_ids)]
+
+mapping_df = ann[["ID", gene_symbol_col]].rename(columns={gene_symbol_col: "Gene"}).copy()
+# Uppercase to aid extraction of symbols from descriptive text as needed
+mapping_df["Gene"] = mapping_df["Gene"].astype(str).str.strip().str.upper()
+mapping_df = mapping_df[(mapping_df["ID"] != "") & (mapping_df["Gene"] != "")]
+
+# Apply mapping using helper (extracts human-like symbols from text and aggregates)
+mapped_gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+n_probes = len(gene_data.index)
+n_mapped = mapping_df["ID"].nunique()
+n_genes = mapped_gene_data.shape[0]
+print(f"Mapping diagnostics: probes_in_expr={n_probes}, probes_mapped={n_mapped}, genes_after_mapping={n_genes}, platform={best_block['platform']}, id_col={probe_col}, symbol_col={gene_symbol_col}, symbol_hits_in_sample={score}")
+
+if n_mapped == 0 or n_genes == 0:
+    raise RuntimeError("Gene mapping resulted in zero genes after selecting the best platform table. Please verify platform/ID matching.")
+
+# Overwrite gene_data with gene-level expression
+gene_data = mapped_gene_data

output/preprocess/Huntingtons_Disease/code/GSE26927.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+cohort = "GSE26927"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
+in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE26927"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/GSE26927.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/GSE26927.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/GSE26927.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine gene expression availability based on platform description (Illumina HumanRef8 v2 BeadChip: mRNA expression)
+is_gene_available = True
+
+# Identify rows for variables from the provided Sample Characteristics Dictionary
+trait_row = 0     # disease field
+age_row = 2       # age at death (in years)
+gender_row = 1    # gender
+
+# Conversion helpers
+def _after_colon(value):
+    if value is None:
+        return None
+    if not isinstance(value, str):
+        return value
+    parts = value.split(":", 1)
+    v = parts[1].strip() if len(parts) > 1 else value.strip()
+    # Normalize common unknown markers
+    if v in {"?", "NA", "N/A", "", "nan", "NaN", "None"}:
+        return None
+    return v
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map Huntington's disease to 1; all other diseases to 0
+    if "huntington" in vl:
+        return 1
+    # For any other disease label, map to 0
+    return 0
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract numeric age; tolerate strings like "82"
+    try:
+        return float(v)
+    except Exception:
+        # Try to extract the first number from the string
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        if m:
+            try:
+                return float(m.group(0))
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"f", "female"}:
+        return 0
+    if vl in {"m", "male"}:
+        return 1
+    return None
+
+# Trait availability: determined by whether trait_row is not None
+is_trait_available = trait_row is not None
+
+# Save initial metadata (initial filtering)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction if clinical data (trait) is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify appropriate columns for mapping: probe IDs and gene symbols
+prob_col = 'ID'       # Matches probe identifiers like ILMN_10000 in gene expression data
+gene_col = 'SYMBOL'   # Contains human gene symbols
+
+# Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Trait defined as Huntington's disease vs other neurodegenerative diseases; post-mortem brain tissue; Illumina HumanRef8 v2 platform."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Huntingtons_Disease/code/GSE34201.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+cohort = "GSE34201"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
+in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE34201"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/GSE34201.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/GSE34201.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/GSE34201.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Expression profiling was performed on ES cells and NSC progeny
+
+# 2) Variable availability and converters
+# From the provided Sample Characteristics Dictionary:
+# 1: 'hd genotype: HD' / 'hd genotype: wild type'  -> trait
+# 3: 'gender: male' / 'gender: female'             -> gender
+trait_row = 1
+age_row = None
+gender_row = 3
+
+def _get_value_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    x = str(x)
+    parts = x.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip() if isinstance(val, str) else val
+
+def convert_trait(x):
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    # Map HD vs WT
+    if v in {"hd", "huntington", "huntington's", "huntingtons", "case", "mutant", "mutation", "affected"}:
+        return 1
+    if v in {"wild type", "wild-type", "wildtype", "wt", "control", "normal", "unaffected"}:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not available in this dataset; function provided for API completeness
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    # Try to parse a number if present
+    m = re.search(r"([-+]?\d*\.?\d+)", str(val))
+    return float(m.group(1)) if m else None
+
+def convert_gender(x):
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    if v in {"male", "m"}:
+        return 1
+    if v in {"female", "f"}:
+        return 0
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for probe IDs and gene symbols from the annotation preview:
+# Probe identifier column: 'ID' (e.g., ILMN_#######)
+# Gene symbol column: 'Symbol'
+
+# 1-2) Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Optionally save the processed gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data (fix variable name to selected_clinical_df)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Optional quick preview to verify alignment
+print(preview_df(linked_data, n=3))
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_final = normalized_gene_data.shape[0] > 0
+is_trait_available_final = trait in selected_clinical_df.index
+note_msg = "INFO: Age not provided; used Gender as covariate only."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, is_gene_available_final, is_trait_available_final,
+    is_trait_biased, unbiased_linked_data, note=note_msg
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Huntingtons_Disease/code/GSE34721.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+cohort = "GSE34721"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
+in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE34721"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/GSE34721.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/GSE34721.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/GSE34721.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# Determine data availability based on provided background and characteristics
+is_gene_available = True  # Gene expression dataset (mRNA) per background info
+
+# Identify rows in the sample characteristics dictionary
+trait_row = 1       # 'htt cag repeat length (longer allele)'
+age_row = None      # No age information available
+gender_row = 0      # 'gender'
+
+# Converters
+def _after_colon(value):
+    if value is None:
+        return None
+    try:
+        s = str(value)
+    except Exception:
+        return None
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Map status from HTT CAG repeat length on the longer allele:
+    # Commonly, >=36 repeats indicates an expanded allele consistent with HD mutation carrier (1),
+    # while <36 is non-expanded (0). Borderline 36–39 can be reduced penetrance but treated as 1 here.
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    try:
+        n = int(str(v).strip())
+    except Exception:
+        import re
+        m = re.search(r'-?\d+', str(v))
+        if not m:
+            return None
+        try:
+            n = int(m.group(0))
+        except Exception:
+            return None
+    return 1 if n >= 36 else 0
+
+def convert_age(x):
+    # Age not available in this dataset
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).strip().lower()
+    if v_low in {'female', 'f'}:
+        return 0
+    if v_low in {'male', 'm'}:
+        return 1
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction if available
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+import re
+
+# Given identifiers from the previous step
+gene_ids = ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',
+            '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',
+            '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',
+            '1552263_at', '1552264_a_at', '1552266_at']
+
+affy_pattern = re.compile(r'^\d+(_[a-z])?_at$')
+affy_like = sum(bool(affy_pattern.match(x)) for x in gene_ids)
+requires_gene_mapping = affy_like / len(gene_ids) > 0.5
+
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns in the annotation dataframe
+probe_col = 'ID'            # Matches probe identifiers in expression data (e.g., '1007_s_at')
+gene_symbol_col = 'Gene Symbol'  # Contains human gene symbols
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Cast flags explicitly to native Python bool to avoid JSON serialization issues
+is_gene_available_flag = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_flag = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+is_trait_biased = bool(is_trait_biased)
+
+note = ("INFO: Affymetrix probe IDs mapped to gene symbols using SOFT annotation. "
+        "Trait (Huntingtons_Disease) derived from HTT CAG repeat length on longer allele (>=36 as 1, else 0). "
+        "Age not available; Gender included.")
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Huntingtons_Disease/code/GSE71220.py

@@ -0,0 +1,136 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+cohort = "GSE71220"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
+in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE71220"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/GSE71220.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/GSE71220.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/GSE71220.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix Human Gene 1.1 ST microarray => gene expression data present
+
+# 2. Variable Availability and Data Type Conversion
+
+# Trait: Huntington's Disease (not present in this COPD/statin dataset)
+trait_row = None  # No field corresponds to Huntington's Disease in this dataset
+
+# Age
+age_row = 2  # 'age: <number>'
+
+# Gender
+gender_row = 3  # 'Sex: M' / 'Sex: F'
+
+# Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = Huntington's Disease case, 0 = non-HD (control/other diseases)
+    v = _after_colon(x)
+    if v is None or v == "" or str(v).lower() in {"na", "nan", "none"}:
+        return None
+    s = str(v).strip().lower()
+    # Positive indicators for Huntington's Disease
+    hd_pos = ["huntington", "huntington's", "huntingtons", "hd (huntington", "hd patient"]
+    if any(tok in s for tok in hd_pos):
+        return 1
+    # Negative indicators (controls or other diseases)
+    neg_tokens = ["control", "healthy", "no", "normal", "copd", "parkinson", "aml", "statin user", "statin", "y", "n"]
+    if any(tok in s for tok in neg_tokens):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip()
+    if s.lower() in {"na", "nan", ""}:
+        return None
+    # Extract numeric part
+    try:
+        return float(s)
+    except Exception:
+        # Attempt to extract numbers from strings like "63 years"
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", s)
+        if m:
+            try:
+                return float(m.group())
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"m", "male", "1"}:
+        return 1
+    if s in {"f", "female", "0"}:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip if trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Huntingtons_Disease/code/GSE95843.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+cohort = "GSE95843"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
+in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE95843"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/GSE95843.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/GSE95843.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/GSE95843.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine data availability and define conversion functions
+
+# Try to use existing sample_char_dict if available; otherwise, define from provided output
+try:
+    sample_char_dict
+except NameError:
+    sample_char_dict = {
+        0: [
+            'plasma donor amyloid beta 42 level (pg/ml): 114.56',
+            'plasma donor amyloid beta 42 level (pg/ml): 77.86',
+            'plasma donor amyloid beta 42 level (pg/ml): 126.36',
+            'plasma donor amyloid beta 42 level (pg/ml): 68.18',
+            'plasma donor amyloid beta 42 level (pg/ml): 183.68',
+            'plasma donor amyloid beta 42 level (pg/ml): 122.5',
+            'plasma donor amyloid beta 42 level (pg/ml): 91.48',
+            'plasma donor amyloid beta 42 level (pg/ml): 138.2',
+            'plasma donor amyloid beta 42 level (pg/ml): 189.32',
+            'plasma donor amyloid beta 42 level (pg/ml): 187.22',
+            'plasma donor amyloid beta 42 level (pg/ml): 187.89',
+            'plasma donor amyloid beta 42 level (pg/ml): 157.07',
+            'plasma donor amyloid beta 42 level (pg/ml): 165.57',
+            'plasma donor amyloid beta 42 level (pg/ml): 162.6',
+            'plasma donor amyloid beta 42 level (pg/ml): 44.72',
+            'plasma donor amyloid beta 42 level (pg/ml): 154.49',
+            'plasma donor amyloid beta 42 level (pg/ml): 152.31',
+            'plasma donor amyloid beta 42 level (pg/ml): 184.5',
+            'plasma donor amyloid beta 42 level (pg/ml): 106.86',
+            'plasma donor amyloid beta 42 level (pg/ml): 102.43',
+            'plasma donor amyloid beta 42 level (pg/ml): 69.45',
+            'plasma donor amyloid beta 42 level (pg/ml): 155.02',
+            'plasma donor amyloid beta 42 level (pg/ml): 114.46',
+            'plasma donor amyloid beta 42 level (pg/ml): 146.74',
+            'plasma donor amyloid beta 42 level (pg/ml): 158.9',
+            'plasma donor amyloid beta 42 level (pg/ml): 89.9',
+            'plasma donor amyloid beta 42 level (pg/ml): 130.07',
+            'plasma donor amyloid beta 42 level (pg/ml): 113.48',
+            'plasma donor amyloid beta 42 level (pg/ml): 72.38',
+            'plasma donor amyloid beta 42 level (pg/ml): 146.32'
+        ]
+    }
+
+# 1) Gene expression availability (based on series title/content; not miRNA/methylation)
+is_gene_available = True
+
+# 2) Variable availability:
+# Only amyloid-beta levels are present; no HD trait, age, or gender fields.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Conversion functions
+import re
+from typing import Optional
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(x) -> Optional[int]:
+    v = _after_colon(x)
+    if v is None:
+        return None
+    val = v.lower().strip()
+    # Map common case/control or HD annotations to binary
+    positives = ['hd', "huntington", "huntington's", 'case', 'patient', 'affected', 'disease', 'yes', 'y', 'positive', 'pos']
+    negatives = ['control', 'healthy', 'normal', 'unaffected', 'no', 'n', 'negative', 'neg', 'non-hd', 'non hd', 'wildtype', 'wt']
+    if any(p in val for p in positives):
+        return 1
+    if any(n in val for n in negatives):
+        return 0
+    return None
+
+def convert_age(x) -> Optional[float]:
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    age = float(m.group(1))
+    if 0 < age < 120:
+        return age
+    return None
+
+def convert_gender(x) -> Optional[int]:
+    v = _after_colon(x)
+    if v is None:
+        return None
+    val = v.lower().strip()
+    if val in ['female', 'f', 'woman', 'girl', 'xx']:
+        return 0
+    if val in ['male', 'm', 'man', 'boy', 'xy']:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction: skipped because trait_row is None

output/preprocess/Huntingtons_Disease/code/TCGA.py

@@ -0,0 +1,75 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Huntingtons_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Huntingtons_Disease/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Huntingtons_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Huntingtons_Disease/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Huntingtons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Given subdirectories from instruction
+listed_subdirs = [
+    'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Uterine_Carcinosarcoma_(UCS)',
+    'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)', 'TCGA_Testicular_Cancer_(TGCT)',
+    'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Rectal_Cancer_(READ)',
+    'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)',
+    'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)',
+    'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)',
+    'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)',
+    'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Try to use actual filesystem listing when possible; otherwise fallback to provided list
+if os.path.isdir(tcga_root_dir):
+    fs_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+else:
+    fs_subdirs = listed_subdirs
+
+# Attempt to find a TCGA cohort relevant to Huntington's Disease (not a cancer -> likely none)
+trait_synonyms = ["huntington", "huntington's", "huntingtons", "huntington_disease", "hd", "chorea"]
+def normalize_name(s: str) -> str:
+    return ''.join(ch.lower() if ch.isalnum() or ch == '_' else ' ' for ch in s)
+
+selected_dir = None
+for d in fs_subdirs:
+    nd = normalize_name(d)
+    if any(sym in nd for sym in trait_synonyms):
+        selected_dir = d
+        break
+
+if selected_dir is None:
+    # No appropriate TCGA cohort for Huntington's Disease; mark as unavailable and exit gracefully
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort found for Huntington's Disease. Skipping TCGA for this trait.")
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Load dataframes
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Print clinical columns
+    print(clinical_df.columns.tolist())

output/preprocess/Huntingtons_Disease/cohort_info.json

@@ -1,82 +1 @@
-{
-  "GSE95843": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE71220": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE34721": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": true,
-    "sample_size": 227
-  },
-  "GSE34201": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": true,
-    "sample_size": 72
-  },
-  "GSE26927": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE154141": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 18
-  },
-  "GSE135589": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 178
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 702
-  }
-}
+{"GSE95843": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE71220": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE34721": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 227, "note": "INFO: Affymetrix probe IDs mapped to gene symbols using SOFT annotation. Trait (Huntingtons_Disease) derived from HTT CAG repeat length on longer allele (>=36 as 1, else 0). Age not available; Gender included."}, "GSE34201": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 72, "note": "INFO: Age not provided; used Gender as covariate only."}, "GSE26927": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 118, "note": "INFO: Trait defined as Huntington's disease vs other neurodegenerative diseases; post-mortem brain tissue; Illumina HumanRef8 v2 platform."}, "GSE135589": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 178, "note": "INFO: Trait coded as Control=0 and preHD/zHD=1. Age corresponds to 'age at year 1'."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Hutchinson-Gilford_Progeria_Syndrome/clinical_data/GSE84351.csv

@@ -1,3 +1,3 @@
-,Sample_1,Sample_2,Sample_3,Sample_4,Sample_5,Sample_6,Sample_7
-Hutchinson-Gilford_Progeria_Syndrome,1.0,0.0,,,,,
-Gender,1.0,0.0,,,,,
+,GSM2232606,GSM2232607,GSM2232608,GSM2232609,GSM2232610,GSM2232611,GSM2232612,GSM2232613,GSM2232614,GSM2232615,GSM2232616,GSM2232617,GSM2232618,GSM2232619,GSM2232620,GSM2232621,GSM2232622,GSM2232623,GSM2232624,GSM2232625,GSM2232626,GSM2232627,GSM2232628,GSM2232629,GSM2232630,GSM2232631,GSM2232632,GSM2232633,GSM2232634,GSM2232635,GSM2232636,GSM2232637,GSM2232638,GSM2232639,GSM2232640,GSM2232641,GSM2232642,GSM2232643,GSM2232644,GSM2232645,GSM2232646,GSM2232647,GSM2232648,GSM2232649,GSM2232650,GSM2232651,GSM2232652,GSM2232653,GSM2232654,GSM2232655,GSM2232656,GSM2232657,GSM2232658,GSM2232659
+Hutchinson-Gilford_Progeria_Syndrome,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,

output/preprocess/Hutchinson-Gilford_Progeria_Syndrome/code/GSE84351.py

@@ -0,0 +1,348 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hutchinson-Gilford_Progeria_Syndrome"
+cohort = "GSE84351"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hutchinson-Gilford_Progeria_Syndrome"
+in_cohort_dir = "../DATA/GEO/Hutchinson-Gilford_Progeria_Syndrome/GSE84351"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/GSE84351.csv"
+out_gene_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/gene_data/GSE84351.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/clinical_data/GSE84351.csv"
+json_path = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix microarray platform implies gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 2   # 'condition: Normal' vs 'condition: HGPS'
+age_row = None  # No age information in the sample characteristics
+gender_row = 0  # 'Sex: Male', 'Sex: Female', 'Sex: ?'
+
+# 2.2 Data Type Conversion
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Map HGPS/Progeria/Case/Patient to 1; Normal/Control to 0
+    if v_low in {'hgps', 'hutchinson-gilford progeria syndrome', 'progeria', 'affected', 'case', 'patient', 'disease'}:
+        return 1
+    if v_low in {'normal', 'control', 'wildtype', 'wt', 'unaffected', 'healthy', 'non-hgps'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    # Not used since age_row is None; keep for compatibility
+    try:
+        # Remove common units if present
+        v_clean = v.replace('years', '').replace('year', '').replace('yrs', '').replace('yr', '').strip()
+        return float(v_clean)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {'female', 'f'}:
+        return 0
+    if v_low in {'male', 'm'}:
+        return 1
+    if v_low in {'?', 'unknown', 'na', 'n/a'}:
+        return None
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import re
+import pandas as pd
+
+# Preserve the original probe-level data from prior step
+probe_expression_df = gene_data
+
+# Try to load a platform (GPL) SOFT annotation if available; fall back to previously loaded gene_annotation
+try:
+    soft_files = [f for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+    gpl_softs = [os.path.join(in_cohort_dir, f) for f in soft_files if 'gpl' in f.lower()]
+    chosen_annot = None
+    if gpl_softs:
+        try:
+            gpl_annotation = get_gene_annotation(gpl_softs[0])
+            # Prefer the annotation with more informative columns
+            if 'gene_annotation' in locals():
+                chosen_annot = gpl_annotation if gpl_annotation.shape[1] >= gene_annotation.shape[1] else gene_annotation
+            else:
+                chosen_annot = gpl_annotation
+        except Exception:
+            chosen_annot = gene_annotation if 'gene_annotation' in locals() else None
+    else:
+        chosen_annot = gene_annotation if 'gene_annotation' in locals() else None
+except Exception:
+    chosen_annot = gene_annotation if 'gene_annotation' in locals() else None
+
+if chosen_annot is None or not isinstance(chosen_annot, pd.DataFrame) or chosen_annot.empty:
+    # As an extreme fallback, proceed without mapping
+    print("WARNING: No usable platform annotation found. Proceeding with probe-level data (no mapping).")
+    gene_data = probe_expression_df.copy()
+else:
+    gene_annotation = chosen_annot
+
+    # 1) Identify the probe ID column
+    if 'ID' in gene_annotation.columns:
+        id_col = 'ID'
+    else:
+        common_id_cols = ['ID_REF', 'PROBE_ID', 'PROBE SET ID', 'PROBE_SET_ID', 'PROBE SET NAME',
+                          'PROBESET_ID', 'PROBESETNAME', 'REPORTER_ID']
+        id_col = next((c for c in gene_annotation.columns if c in common_id_cols or 'ID' in c.upper()), None)
+        if id_col is None:
+            # fallback: take the first column as ID if it looks like the probe IDs in expression
+            id_col = gene_annotation.columns[0]
+
+    # 2) Try to find a gene symbol column
+    def _norm_col(c: str) -> str:
+        return re.sub(r'[^A-Z0-9]+', '', str(c).upper())
+
+    normalized_map = {_norm_col(c): c for c in gene_annotation.columns}
+    preferred_norm_names = [
+        'GENESYMBOL', 'SYMBOL', 'GENE_SYMBOL', 'GENESYMBOLS', 'GENE_SYMBOLS',
+        'GENEASSIGNMENT', 'GENEASSIGNMENTS', 'GENE', 'GENENAME', 'GENE_NAME'
+    ]
+    gene_col = None
+    for norm_name in preferred_norm_names:
+        if norm_name in normalized_map:
+            gene_col = normalized_map[norm_name]
+            break
+
+    # Heuristic scoring if name-based search failed
+    def _score_gene_col(series: pd.Series) -> float:
+        s = series.dropna().astype(str)
+        if s.empty:
+            return 0.0
+        sample = s.sample(min(len(s), 5000), random_state=0)
+        extracted = sample.map(extract_human_gene_symbols)
+        return (extracted.map(lambda lst: len(lst) > 0)).mean()
+
+    used_symbol_column = False
+    used_refseq_fallback = False
+
+    if gene_col is None:
+        candidates = [c for c in gene_annotation.columns if c != id_col]
+        if candidates:
+            scores = {c: _score_gene_col(gene_annotation[c]) for c in candidates}
+            viable = {c: s for c, s in scores.items() if s >= 0.10}
+            if len(viable) > 0:
+                gene_col = max(viable, key=viable.get)
+                used_symbol_column = True
+
+    # 3) If still no gene symbol column, fall back to RefSeq-like identifiers
+    if gene_col is None:
+        # Prefer GB_ACC or any column that looks like RefSeq
+        refseq_like = [c for c in gene_annotation.columns if _norm_col(c) in {'GBACC', 'REFSEQ', 'REFSEQID', 'REFSEQTRANSCRIPTID'} or 'REFSEQ' in _norm_col(c)]
+        if 'GB_ACC' in gene_annotation.columns:
+            gene_col = 'GB_ACC'
+        elif refseq_like:
+            gene_col = refseq_like[0]
+        else:
+            # As a last resort, use SPOT_ID (genomic range), acknowledging it's not a true gene ID
+            gene_col = 'SPOT_ID' if 'SPOT_ID' in gene_annotation.columns else candidates[0] if candidates else gene_annotation.columns[0]
+        used_refseq_fallback = True
+
+    # Build mapping dataframe using the chosen columns
+    mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+
+    # Remove obviously empty/placeholder entries
+    if not mapping_df.empty:
+        mapping_df['Gene'] = mapping_df['Gene'].astype(str)
+        mapping_df = mapping_df[~mapping_df['Gene'].isin(['', 'nan', 'NaN', 'None'])]
+
+    # Define a generic mapping function that does not strip non-symbol IDs
+    def apply_probe_mapping_generic(expression_df: pd.DataFrame, mapping_df: pd.DataFrame) -> pd.DataFrame:
+        mapping_df = mapping_df[mapping_df['ID'].isin(expression_df.index)].copy()
+        if mapping_df.empty:
+            return pd.DataFrame()
+        # Split multi-mapped entries on common delimiters (avoid splitting on underscore)
+        delim = r'\s*(?:///|/|;|,|\|)\s*'
+        mapping_df['Gene'] = mapping_df['Gene'].astype(str).str.strip()
+        mapping_df = mapping_df[mapping_df['Gene'] != '']
+        mapping_df['Gene'] = mapping_df['Gene'].str.split(delim)
+        mapping_df['num_genes'] = mapping_df['Gene'].apply(lambda lst: len([g for g in lst if g]))
+        mapping_df = mapping_df.explode('Gene')
+        mapping_df = mapping_df.dropna(subset=['Gene'])
+        mapping_df['Gene'] = mapping_df['Gene'].astype(str).str.strip()
+        mapping_df = mapping_df[mapping_df['Gene'] != '']
+        if mapping_df.empty:
+            return pd.DataFrame()
+        mapping_df.set_index('ID', inplace=True)
+
+        merged_df = mapping_df.join(expression_df)
+        expr_cols = [col for col in merged_df.columns if col not in ['Gene', 'num_genes']]
+        if len(expr_cols) == 0:
+            return pd.DataFrame()
+        merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)
+        gene_expression_df = merged_df.groupby('Gene')[expr_cols].sum()
+        return gene_expression_df
+
+    # 4) Apply mapping according to the chosen strategy
+    if not used_refseq_fallback:
+        # Use symbol-extracting helper when we believe we have symbols/gene assignments
+        print(f"Using annotation columns -> ID: '{id_col}', Gene symbols: '{gene_col}' (symbol-based mapping)")
+        try:
+            gene_data_mapped = apply_gene_mapping(expression_df=probe_expression_df, mapping_df=mapping_df)
+        except Exception:
+            gene_data_mapped = pd.DataFrame()
+    else:
+        # Use generic mapping with RefSeq or coordinate-based IDs
+        print(f"WARNING: No clear gene symbol column found. Falling back to '{gene_col}' as gene identifier.")
+        print(f"Using annotation columns -> ID: '{id_col}', Gene IDs: '{gene_col}' (generic mapping)")
+        gene_data_mapped = apply_probe_mapping_generic(expression_df=probe_expression_df, mapping_df=mapping_df)
+
+    # 5) Final fallback if mapping fails
+    if gene_data_mapped is None or gene_data_mapped.empty:
+        print("WARNING: Mapping produced no gene-level data. Falling back to probe-level measurements.")
+        gene_data = probe_expression_df.copy()
+    else:
+        gene_data = gene_data_mapped
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data (fix variable name)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Compute availability flags for final validation
+is_gene_available_final = (normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in selected_clinical_df.index) and (selected_clinical_df.loc[trait].notna().any())
+
+# Prepare an informative note
+retained_pct = None
+try:
+    retained_pct = (normalized_gene_data.shape[0] / gene_data.shape[0]) if gene_data.shape[0] > 0 else None
+except Exception:
+    retained_pct = None
+
+if retained_pct is None:
+    note = "INFO: Normalization completed; unable to compute retained gene proportion."
+else:
+    note = f"INFO: Normalization retained {retained_pct:.2%} of rows after mapping to standardized symbols."
+    if retained_pct < 0.05:
+        note = ("WARNING: Normalization retained fewer than 5% of rows after symbol mapping; "
+                "platform annotation likely used RefSeq/coordinate IDs causing most rows to be dropped. "
+                + note)
+
+# 5. Conduct quality check and save the cohort information.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Hutchinson-Gilford_Progeria_Syndrome/code/GSE84360.py

@@ -0,0 +1,222 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hutchinson-Gilford_Progeria_Syndrome"
+cohort = "GSE84360"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hutchinson-Gilford_Progeria_Syndrome"
+in_cohort_dir = "../DATA/GEO/Hutchinson-Gilford_Progeria_Syndrome/GSE84360"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/GSE84360.csv"
+out_gene_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/gene_data/GSE84360.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/clinical_data/GSE84360.csv"
+json_path = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Gene expression data availability
+is_gene_available = True  # The series involves patient-derived cells; likely includes gene expression rather than pure miRNA/methylation only.
+
+# 2) Variable availability based on the provided Sample Characteristics Dictionary
+# Keys observed:
+# 0: Sex: Male/Female/?
+# 1: cell line: ...
+# 2: condition: Normal/HGPS
+# 3: cell type: ...
+trait_row = 2
+age_row = None
+gender_row = 0
+
+# 2.2) Conversion functions
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val).strip().strip('"').strip("'")
+    if ":" in s:
+        s = s.split(":", 1)[1]
+    return s.strip()
+
+def convert_trait(val):
+    v = _after_colon(val)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Positive for HGPS cases
+    if any(k in vl for k in ["hgps", "hutchinson", "progeria"]):
+        return 1
+    # Negative for controls
+    if vl in ["normal", "control", "wildtype", "wt", "healthy", "unaffected", "non-hgps"]:
+        return 0
+    return None
+
+def convert_age(val):
+    v = _after_colon(val)
+    if v is None:
+        return None
+    # Extract numeric component if present
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(val):
+    v = _after_colon(val)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in ["male", "m", "man", "boy"]:
+        return 1
+    if vl in ["female", "f", "woman", "girl"]:
+        return 0
+    if vl in ["?", "unknown", "na", "n/a", "not available", "undetermined", "u"]:
+        return None
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    import os
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import re
+
+# Determine the probe identifier column in the annotation (must match expression row index)
+id_col = 'ID' if 'ID' in gene_annotation.columns else gene_annotation.columns[0]
+
+# Identify a column that likely contains human gene symbols using heuristic hits
+def symbol_hit_score(series, sample_n=1000):
+    sample = series.dropna().astype(str).head(sample_n)
+    hits = sample.map(lambda x: len(extract_human_gene_symbols(x)))
+    return int(hits.sum())
+
+text_cols = [c for c in gene_annotation.columns if getattr(gene_annotation[c], 'dtype', None) == object]
+best_col = None
+best_score = -1
+for c in text_cols:
+    try:
+        score = symbol_hit_score(gene_annotation[c])
+    except Exception:
+        score = -1
+    if score > best_score:
+        best_score = score
+        best_col = c
+
+# Prefer explicit symbol-like columns if they exist, otherwise rely on best_col by score
+preferred_order = [c for c in gene_annotation.columns if re.search(r'(symbol|gene.*name|gene)', c, flags=re.I)]
+gene_col = None
+for c in preferred_order:
+    if c in text_cols and symbol_hit_score(gene_annotation[c]) > 0:
+        gene_col = c
+        break
+if gene_col is None and best_score > 0:
+    gene_col = best_col
+
+print(f"Chosen identifier column (probe/feature IDs): {id_col}")
+print(f"Detected gene symbol column: {gene_col if gene_col is not None else 'None'}")
+
+mapped_successfully = False
+
+# Attempt 1: map using detected gene symbol column
+if gene_col is not None:
+    mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+    mapped_gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+    if mapped_gene_data.shape[0] > 0 and mapped_gene_data.shape[1] == gene_data.shape[1]:
+        gene_data = mapped_gene_data
+        mapped_successfully = True
+        print(f"Mapping succeeded using gene symbol column: {gene_col} "
+              f"-> gene-level rows: {gene_data.shape[0]}, samples: {gene_data.shape[1]}")
+    else:
+        print("WARNING: Mapping via detected gene symbol column produced empty/invalid result.")
+
+# Attempt 2 (fallback): try RefSeq accession column (GB_ACC) if present, then extract human symbols
+# Note: Many tiling/region arrays only have accessions or genomic coordinates; this may still fail to yield symbols.
+if not mapped_successfully and 'GB_ACC' in gene_annotation.columns:
+    print("Attempting fallback mapping via GB_ACC (RefSeq accessions) -> human symbol extraction.")
+    mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col='GB_ACC')
+    mapped_gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+    if mapped_gene_data.shape[0] > 0 and mapped_gene_data.shape[1] == gene_data.shape[1]:
+        gene_data = mapped_gene_data
+        mapped_successfully = True
+        print(f"Mapping succeeded using GB_ACC fallback -> gene-level rows: {gene_data.shape[0]}, "
+              f"samples: {gene_data.shape[1]}")
+    else:
+        print("WARNING: GB_ACC-based mapping did not yield valid human gene symbols.")
+
+# If mapping still failed, raise an error instead of silently keeping probe-level data
+if not mapped_successfully:
+    raise RuntimeError(
+        "Gene symbol mapping failed: No suitable gene symbol column found in the platform annotation, "
+        "and fallback via GB_ACC (RefSeq) did not yield valid symbols. "
+        "This platform likely lacks direct gene symbol annotations (e.g., tiling/genome-region array). "
+        "Cannot proceed with gene-level analysis."
+    )

output/preprocess/Hutchinson-Gilford_Progeria_Syndrome/code/TCGA.py

@@ -0,0 +1,68 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hutchinson-Gilford_Progeria_Syndrome"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Hutchinson-Gilford_Progeria_Syndrome/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Identify the most relevant TCGA cohort directory for Hutchinson-Gilford Progeria Syndrome (unlikely in TCGA)
+search_terms = {
+    "hutchinson", "gilford", "progeria", "hgps", "progeroid", "lamin a", "lamina", "lmna"
+}
+
+selected_dir = None
+try:
+    subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+    # Score directories by number of matched keywords; choose the highest scoring one
+    scored = []
+    for d in subdirs:
+        dl = d.lower()
+        score = sum(1 for t in search_terms if t in dl)
+        if score > 0:
+            scored.append((score, d))
+    if scored:
+        scored.sort(key=lambda x: (-x[0], len(x[1])))
+        selected_dir = scored[0][1]
+except Exception:
+    selected_dir = None
+
+if not selected_dir:
+    # No suitable TCGA cohort for this trait; record and skip
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA_NoMatchingCohort",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    try:
+        clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+        clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+        genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+        print(list(clinical_df.columns))
+    except Exception:
+        # If file discovery or loading fails, record and skip
+        _ = validate_and_save_cohort_info(
+            is_final=False,
+            cohort=selected_dir,
+            info_path=json_path,
+            is_gene_available=False,
+            is_trait_available=False
+        )

output/preprocess/Hutchinson-Gilford_Progeria_Syndrome/cohort_info.json

@@ -1,32 +1 @@
-{
-  "GSE84360": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": true,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE84351": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE84351": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "WARNING: Normalization retained fewer than 5% of rows after symbol mapping; platform annotation likely used RefSeq/coordinate IDs causing most rows to be dropped. INFO: Normalization retained 0.00% of rows after mapping to standardized symbols."}, "TCGA_NoMatchingCohort": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}